相关申请的交叉引用CROSS-REFERENCE TO RELATED APPLICATIONS
本申请要求申请日为2023年10月07日，申请号为202311284381.6，名称为“一种游戏中的原创内容推荐方法、装置、电子设备及介质”的中国专利申请的优先权，该中国专利申请的全部内容通过引用结合在本文中。This application claims priority to a Chinese patent application filed on October 7, 2023, with application number 202311284381.6, entitled “A method, device, electronic device and medium for recommending original content in a game”, the entire contents of which are incorporated herein by reference.
本公开涉及计算机技术领域，具体而言，涉及一种游戏中的原创内容推荐方法、装置、电子设备及介质。The present disclosure relates to the field of computer technology, and in particular to a method, device, electronic device, and medium for recommending original content in a game.
随着游戏市场的不断发展，UGC(User Generated Content，用户产生内容)类游戏逐渐进入用户(玩家)的视野中。得益于游戏工具和引擎的演化进步，用户现在可以更加容易地创建和分享自己制作的游戏内容。在UGC类游戏中，随着用户创建的UGC内容数量越来越多，种类越来越丰富，一个良好的UGC推荐系统，可以构成用户和UGC内容之间的桥梁，增加UGC类游戏的互动程度，如何将合适的UGC内容推荐给不同的用户成为了游戏中至关重要的环节。With the continuous development of the game market, UGC (User Generated Content) games have gradually entered the field of vision of users (players). Thanks to the evolution of game tools and engines, users can now more easily create and share their own game content. In UGC games, as the number and types of UGC content created by users increase, a good UGC recommendation system can serve as a bridge between users and UGC content, increase the level of interaction in UGC games, and how to recommend appropriate UGC content to different users has become a crucial link in the game.
发明内容Summary of the invention
本公开提供一种游戏中的原创内容推荐方法、装置、电子设备及介质。The present disclosure provides a method, device, electronic device and medium for recommending original content in a game.
第一方面，本公开实施例提供了一种游戏中的原创内容推荐方法，包括：In a first aspect, an embodiment of the present disclosure provides a method for recommending original content in a game, comprising:
按照第一预设推荐比例分别从不同等级的每个第一内容池中选取近期原创内容推荐给用户；Selecting recent original content from each first content pool of different levels according to a first preset recommendation ratio and recommending it to the user;
根据用户针对目标近期原创内容的反馈信息确定近期劣质内容，将近期劣质内容从多个第一内容池中移除；Determine recent low-quality content based on user feedback information on target recent original content, and remove the recent low-quality content from the multiple first content pools;
获取新的近期原创内容补充至多个第一内容池中的至少一个目标第一内容池，按照推荐规则将新的近期原创内容推荐给用户，新的近期原创内容为未放入过多个第一内容池中的近期原创内容。New recent original content is obtained and added to at least one target first content pool among the multiple first content pools, and the new recent original content is recommended to the user according to the recommendation rule. The new recent original content is recent original content that has not been placed in the multiple first content pools.
第二方面，本公开实施例还提供了一种游戏中的原创内容推荐装置，所述装置包括：In a second aspect, the embodiments of the present disclosure further provide a device for recommending original content in a game, the device comprising:
第一推荐模块，用于按照第一预设推荐比例分别从不同等级的每个第一内容池中选取近期原创内容推荐给用户；A first recommendation module, configured to select recent original content from each first content pool of different levels and recommend it to the user according to a first preset recommendation ratio;
内容更新模块，用于根据用户针对目标近期原创内容的反馈信息确定近期劣质内容，将所述近期劣质内容从多个第一内容池中移除；A content updating module, configured to determine recent low-quality content according to user feedback information on target recent original content, and remove the recent low-quality content from the plurality of first content pools;
第二推荐模块，用于获取新的近期原创内容补充至所述多个第一内容池中的至少一个目标第一内容池，按照推荐规则将所述新的近期原创内容推荐给用户，所述新的近期原创内容为未放入过所述多个第一内容池中的近期原创内容。The second recommendation module is used to obtain new recent original content to supplement at least one target first content pool among the multiple first content pools, and recommend the new recent original content to the user according to the recommendation rules. The new recent original content is recent original content that has not been placed in the multiple first content pools.
第三方面，本公开实施例还提供一种电子设备，包括：处理器、存储器和总线，所述存储器存储有所述处理器可执行的机器可读指令，当电子设备运行时，所述处理器与所述存储器之间通过总线通信，所述机器可读指令被所述处理器执行时执行如上述的游戏中的原创内容推荐方法的步骤。In a third aspect, an embodiment of the present disclosure further provides an electronic device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the original content recommendation method in the game as described above are performed.
第四方面，本公开实施例还提供一种计算机可读存储介质，该计算机可读存储介质上存储有计算机程序，该计算机程序被处理器运行时执行如上述的游戏中的原创内容推荐方法的步骤。In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the original content recommendation method in the game as described above are executed.
本公开实施例带来了以下有益效果：The embodiments of the present disclosure bring the following beneficial effects:
本公开实施例提供的一种游戏中的原创内容推荐方法、装置、电子设备及介质，能够直接获取新的近期原创内容，将新的近期原创内容推荐给用户，使得刚注册的用户能够获取到新的近期原创内容，也可以使新的近期发布内容得以展示，避免了出现冷启动，同时，能够从多个内容池中选取处于不同用户喜爱度区间的近期原创内容推荐给用户，增加了推荐的广度，与现有技术中的游戏中的原创内容推荐方法相比，解决了现有推荐方法存在冷启动、过度推荐和狭窄推荐的问题。The embodiments of the present disclosure provide a method, device, electronic device and medium for recommending original content in a game, which can directly obtain new recent original content and recommend the new recent original content to users, so that newly registered users can obtain new recent original content, and can also display new recently released content, thereby avoiding cold start. At the same time, recent original content in different user preference ranges can be selected from multiple content pools and recommended to users, thereby increasing the breadth of recommendation. Compared with the original content recommendation method in the game in the prior art, the problems of cold start, over-recommendation and narrow recommendation in the existing recommendation method are solved.
图1示出了本公开实施例其中之一种游戏中的原创内容推荐方法的流程图；FIG1 shows a flow chart of a method for recommending original content in a game according to an embodiment of the present disclosure;
图2示出了本公开实施例其中之一种多个第一内容池中近期原创内容更新过程的示意图；FIG2 is a schematic diagram showing a recent original content update process in a plurality of first content pools according to an embodiment of the present disclosure;
图3示出了本公开实施例其中之一种计算喜爱度评分的流程图；FIG3 shows a flowchart of calculating a likeability score according to one embodiment of the present disclosure;
图4示出了本公开实施例其中之一种游戏中的原创内容推荐装置的结构示意图；FIG4 is a schematic diagram showing the structure of an original content recommendation device in a game according to one embodiment of the present disclosure;
图5示出了本公开实施例其中之一种电子设备的结构示意图。FIG. 5 shows a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
为使本公开实施例的目的、技术方案和优点更加清楚，下面将结合本公开实施例中附图，对本公开实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本公开一部分实施例，而不是全部的实施例。通常在此处附图中描述和示出的本公开实施例的组件可以以各种不同的配置来布置和设计。因此，以下对在附图中提供的本公开的实施例的详细描述并非旨在限制要求保护的本公开的范围，而是仅仅表示本公开的选定实施例。基于本公开的实施例，本领域技术人员在没有做出创造性劳动的前提下所获得的每个其他实施例，都属于本公开保护的范围。In order to make the purpose, technical scheme and advantages of the embodiments of the present disclosure clearer, the technical scheme in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all of the embodiments. The components of the embodiments of the present disclosure generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present disclosure provided in the drawings is not intended to limit the scope of the present disclosure for protection, but merely represents the selected embodiments of the present disclosure. Based on the embodiments of the present disclosure, each other embodiment obtained by those skilled in the art without making creative work belongs to the scope of protection of the present disclosure.
推荐技术方案主要分为三种类型：基于用户相似度的推荐、基于物品相似度的推荐以及基于协同过滤的推荐，上述推荐算法均为个性化推荐方式，即通过分析用户行为，找出与目标用户兴趣相似的内容，从而进行推荐。Recommendation technology solutions are mainly divided into three types: recommendation based on user similarity, recommendation based on item similarity, and recommendation based on collaborative filtering. The above recommendation algorithms are all personalized recommendation methods, that is, by analyzing user behavior, finding content similar to the interests of the target user, and then making recommendations.
然而，上述推荐方法需要利用大量的用户数据进行模型训练，存在严重的冷启动问题，面对刚注册的用户和刚发布的内容，无法进行有效的推荐。同时，推荐与用户兴趣相似的内容，容易出现过度推荐和狭窄推荐的问题。However, the above recommendation methods require a large amount of user data for model training, which has a serious cold start problem. When facing newly registered users and newly published content, it is impossible to make effective recommendations. At the same time, recommending content similar to user interests is prone to problems of over-recommendation and narrow recommendation.
鉴于上述问题，本公开提供一种游戏中的原创内容推荐方法、装置、电子设备及介质。In view of the above problems, the present disclosure provides a method, device, electronic device and medium for recommending original content in a game.
在本公开其中一种实施例中的游戏中的原创内容推荐方法可以运行于本地终端设备或者是服务器。当游戏中的原创内容推荐方法运行于服务器时，该方法则可以基于云交互系统来实现与执行，其中，云交互系统包括服务器和客户端设备。In one embodiment of the present disclosure, the original content recommendation method in the game can be run on a local terminal device or a server. When the original content recommendation method in the game is run on a server, the method can be implemented and executed based on a cloud interaction system, wherein the cloud interaction system includes a server and a client device.
在一可选的实施方式中，云交互系统下可以运行各种云应用，例如：云游戏。以云游戏为例，云游戏是指以云计算为基础的游戏方式。在云游戏的运行模式下，游戏程序的运行主体和游戏画面呈现主体是分离的，游戏中的原创内容推荐方法的储存与运行是在云游戏服务器上完成的，客户端设备的作用用于数据的接收、发送以及游戏画面的呈现，举例而言，客户端设备可以是靠近用户侧的具有数据传输功能的显示设备，如，移动终端、电视机、计算机、掌上电脑等；但是进行信息处理的为云端的云游戏服务器。在进行游戏时，玩家操作客户端设备向云游戏服务器发送操作指令，云游戏服务器根据操作指令运行游戏，将游戏画面等数据进行编码压缩，通过网络返回客户端设备，最后，通过客户端设备进行解码并输出游戏画面。In an optional implementation, various cloud applications can be run under the cloud interaction system, such as cloud games. Taking cloud games as an example, cloud games refer to a game mode based on cloud computing. In the operation mode of cloud games, the operating body of the game program and the main body of the game screen presentation are separated. The storage and operation of the original content recommendation method in the game are completed on the cloud game server. The role of the client device is used for receiving and sending data and presenting the game screen. For example, the client device can be a display device with data transmission function close to the user side, such as a mobile terminal, a TV, a computer, a handheld computer, etc.; but the cloud game server in the cloud is used for information processing. When playing the game, the player operates the client device to send an operation instruction to the cloud game server. The cloud game server runs the game according to the operation instruction, encodes and compresses the game screen and other data, and returns it to the client device through the network. Finally, the client device decodes and outputs the game screen.
在一可选的实施方式中，以游戏为例，本地终端设备存储有游戏程序并用于呈现游戏画面。本地终端设备用于通过图形用户界面与玩家进行交互，即，常规的通过电子设备下载安装游戏程序并运行。该本地终端设备将图形用户界面提供给玩家的方式可以包括多种，例如，可以渲染显示在终端的显示屏上，或者，通过全息投影提供给玩家。举例而言，本地终端设备可以包括显示屏和处理器，该显示屏用于呈现图形用户界面，该图形用户界面包括游戏画面，该处理器用于运行该游戏、生成图形用户界面以及控制图形用户界面在显示屏上的显示。In an optional embodiment, taking a game as an example, a local terminal device stores a game program and is used to present a game screen. The local terminal device is used to interact with the player through a graphical user interface, that is, the game program is downloaded and installed by an electronic device and run conventionally. The local terminal device may provide the graphical user interface to the player in a variety of ways, for example, it may be rendered and displayed on a display screen of the terminal, or provided to the player through a holographic projection. For example, the local terminal device may include a display screen and a processor, the display screen is used to present a graphical user interface, the graphical user interface includes a game screen, and the processor is used to run the game, generate a graphical user interface, and control the display of the graphical user interface on the display screen.
在一种可能的实施方式中，本公开实施例提供了一种游戏中的原创内容推荐方法，通过终端设备提供图形用户界面，其中，终端设备可以是前述提到的本地终端设备，也可以是前述提到的云交互系统中的客户端设备。In a possible implementation, an embodiment of the present disclosure provides a method for recommending original content in a game, providing a graphical user interface through a terminal device, wherein the terminal device can be the local terminal device mentioned above, or can be a client device in the cloud interaction system mentioned above.
请参阅图1，图1为本公开实施例所提供的一种游戏中的原创内容推荐方法的流程图。如图1所示，本公开实施例提供的游戏中的原创内容推荐方法，包括：Please refer to FIG1 , which is a flow chart of a method for recommending original content in a game provided by an embodiment of the present disclosure. As shown in FIG1 , the method for recommending original content in a game provided by an embodiment of the present disclosure includes:
步骤S101，按照第一预设推荐比例分别从不同等级的每个第一内容池中选取近期原创内容推荐给用户。Step S101 , selecting recent original content from each first content pool of different levels according to a first preset recommendation ratio and recommending it to the user.
在该步骤中，第一预设推荐比例可指从多个第一内容池中分别选取的近期原创内容之间的推荐比例，第一预设推荐比例表征每个第一内容池的推荐权重。In this step, the first preset recommendation ratio may refer to a recommendation ratio between recent original contents respectively selected from a plurality of first content pools, and the first preset recommendation ratio represents a recommendation weight of each first content pool.
第一内容池可指存放近期原创内容的资源池，各个第一内容池之间存放的近期原创内容是不同的，一个近期原创内容只能存放在多个第一内容池中的一个第一内容池中。The first content pool may refer to a resource pool for storing recent original contents. The recent original contents stored in various first content pools are different. A recent original content can only be stored in one of the multiple first content pools.
示例性的，多个第一内容池包括第一内容池D、第一内容池C、第一内容池B、第一内容池A、第一内容池S。Exemplarily, the plurality of first content pools include a first content pool D, a first content pool C, a first content pool B, a first content pool A, and a first content pool S.
用户喜爱度区间可指每个第一内容池对应的用户喜爱度范围，每个第一内容池对应的用户喜爱度区间由该第一内容池中存储的近期原创内容决定。The user preference interval may refer to a user preference range corresponding to each first content pool, and the user preference interval corresponding to each first content pool is determined by recent original content stored in the first content pool.
假设，第一内容池C中存储了100个近期原创内容，这100个近期原创内容的用户喜爱度最大值为10000，最小值为1000，则第一内容池C对应的用户喜爱度区间为1000至10000。Assume that the first content pool C stores 100 recent original contents, the maximum user preference of these 100 recent original contents is 10,000, and the minimum user preference is 1,000. Then the user preference range corresponding to the first content pool C is 1,000 to 10,000.
近期原创内容可指用户最近一段时间内发布的原创内容，其中，最近一段时间是设定的时间范围，例如：将用户在最近7天内发布的原创内容作为近期原创内容。Recent original content may refer to original content published by users in a recent period of time, where the recent period of time is a set time range. For example, original content published by users within the last 7 days is regarded as recent original content.
原创内容可指游戏中用户原创的内容，在UGC类游戏中用户可以将自身创建的内容分享给其他用户进行体验，将这些可供分享体验的内容称为原创内容。Original content can refer to the content created by users in the game. In UGC games, users can share the content they create with other users for experience. These contents that can be shared and experienced are called original content.
示例性的，原创内容可以是UGC类游戏中用户原创的游戏关卡、地图、副本，也可以是用户原创的宠物、物品。For example, original content can be game levels, maps, and copies created by users in UGC games, or pets and items created by users.
在本公开实施例中，为了能够及时将近期发布的原创内容推荐给用户，可针对每个第一内容池，从该第一内容池中存储的近期原创内容中随机选取一定数量的近期原创内容，以推荐给用户，使得推荐给用户的原创内容不但是近期新发布的内容，不同的近期原创内容还处于不同的用户喜爱度区间，增加了内容推荐的多样性，避免狭窄推荐的问题。同时，即使是针对新注册的用户或者刚进入游戏的新用户，也能够将近期新发布的原创内容有效地推荐给他们。In the disclosed embodiment, in order to be able to recommend recently released original content to users in a timely manner, for each first content pool, a certain number of recent original content can be randomly selected from the recent original content stored in the first content pool to be recommended to users, so that the original content recommended to users is not only the recently released content, but also different recent original content is in different user preference intervals, which increases the diversity of content recommendations and avoids the problem of narrow recommendations. At the same time, even for newly registered users or new users who have just entered the game, the recently released original content can be effectively recommended to them.
在一示例中，可以是响应于用户针对内容推荐界面的选取操作，将从多个第一内容池中选取的近期原创内容的内容信息推荐给用户，也可以是在满足特定条件时，系统主动将从多个第一内容池中选取的近期原创内容的内容信息推荐给用户。In one example, in response to a user's selection operation on the content recommendation interface, content information of recent original content selected from multiple first content pools may be recommended to the user, or the system may proactively recommend content information of recent original content selected from multiple first content pools to the user when specific conditions are met.
以上述示例为例，假设第一内容池D、第一内容池C、第一内容池B、第一内容池A、第一内容池S各自的第一预设推荐比例分别为，10％、20％、35％、25％、10％、在选取近期原创内容时，总共需要选取100个近期原创内容推荐给用户，则从第一内容池D中选取100×10％＝10个近期原创内容，从第一内容池C中选取100×20％＝20个近期原创内容，从第一内容池B中选取100×35％＝35个近期原创内容，从第一内容池A中选取100×25％＝25个近期原创内容，从第一内容池S中选取100×10％＝10个近期原创内容。Taking the above example, assuming that the first preset recommendation ratios of the first content pool D, the first content pool C, the first content pool B, the first content pool A, and the first content pool S are 10%, 20%, 35%, 25%, and 10%, respectively, when selecting recent original content, a total of 100 recent original content need to be selected to recommend to users, then 100×10%=10 recent original content are selected from the first content pool D, 100×20%=20 recent original content are selected from the first content pool C, 100×35%=35 recent original content are selected from the first content pool B, 100×25%=25 recent original content are selected from the first content pool A, and 100×10%=10 recent original content are selected from the first content pool S.
需要说明的是，一个第一内容池对应的用户喜爱度区间不是固定的，会随着该第一内容池中存放的近期原创内容的改变而改变。It should be noted that the user preference interval corresponding to a first content pool is not fixed, and will change with the change of recent original content stored in the first content pool.
步骤S102，根据用户针对目标近期原创内容的反馈信息确定近期劣质内容，将近期劣质内容从多个第一内容池中移除。Step S102: determine recent low-quality content based on user feedback information on target recent original content, and remove the recent low-quality content from the plurality of first content pools.
该步骤中，反馈信息可指用户针对目标近期原创内容进行的反馈操作的信息，反馈信息用于表征用户针对目标近期原创内容的满意程度，反馈信息包括但不限于点赞、收藏、分享、游玩。In this step, the feedback information may refer to information about feedback operations performed by users on the target's recent original content. The feedback information is used to represent the user's satisfaction with the target's recent original content. The feedback information includes but is not limited to likes, collections, shares, and plays.
近期劣质内容可指多个第一内容池中存储的所有近期原创内容中用户喜爱度排名最低的预设数量的近期原创内容。The recent low-quality content may refer to a preset number of recent original content with the lowest user preference ranking among all recent original content stored in the plurality of first content pools.
在本公开实施例中，在保证能够将近期新发布的原创内容推荐给用户的同时，也需要保证用户对推荐内容的接受程度，为此，根据不同用户针对目标近期原创内容的反馈操作，来对目标近期原创内容进行用户喜爱度评价，以根据用户喜爱度确定近期劣质内容，并将近期劣质内容从第一内容池中移除，避免将用户喜爱度较低的近期原创内容重复推荐给用户。In the disclosed embodiment, while ensuring that recently released original content can be recommended to users, it is also necessary to ensure the user's acceptance of the recommended content. To this end, the target recent original content is evaluated for user preference based on the feedback operations of different users on the target recent original content, so as to determine recent low-quality content based on user preference, and remove recent low-quality content from the first content pool to avoid repeated recommendation of recent original content with low user preference to users.
以原创内容为游戏关卡为例，将近期发布的游戏关卡推荐给用户之后，部分用户可以在游戏推荐页面中查看到被推荐的游戏关卡的详细信息，例如：关卡名称、关卡截图、关卡简介、关卡的游玩数、关卡的点赞数，用户可以根据自身的喜爱偏好自主地选择是否游玩该关卡，并可在游玩关卡后对该关卡进行点赞、分享、收藏、评价等反馈操作，以表达自己对关卡的喜爱度。根据不同用户的反馈操作，可以综合判断出用户对某一近期原创内容的综合喜爱度，根据不同近期原创内容的综合喜爱度排名，从多个第一内容池存储的所有近期原创内容中选取出近期劣质内容，以将近期劣质内容从多个第一内容池中移除。Taking the original content as game levels as an example, after recommending the recently released game levels to users, some users can view the detailed information of the recommended game levels on the game recommendation page, such as: level name, level screenshots, level introduction, number of level plays, number of level likes, and users can choose whether to play the level according to their own preferences. After playing the level, they can like, share, collect, evaluate and other feedback operations to express their liking for the level. Based on the feedback operations of different users, the comprehensive liking of users for a recent original content can be comprehensively judged. Based on the comprehensive liking ranking of different recent original content, recent low-quality content can be selected from all recent original content stored in multiple first content pools to remove recent low-quality content from multiple first content pools.
步骤S103，获取新的近期原创内容补充至多个第一内容池中的至少一个目标第一内容池，按照推荐规则将新的近期原创内容推荐给用户。Step S103: acquiring new recent original content and adding it to at least one target first content pool among the plurality of first content pools, and recommending the new recent original content to the user according to the recommendation rule.
该步骤中，新的近期原创内容为未放入过多个第一内容池中的近期原创内容。新的近期原创内容是还未向用户推荐过的近期发布的原创内容。In this step, the new recent original content is the recent original content that has not been put into the multiple first content pools. The new recent original content is the recently released original content that has not been recommended to the user.
推荐规则可指推荐新的近期原创内容的规则，推荐规则用于保证新的近期原创内容必然会被推荐给用户。The recommendation rule may refer to a rule for recommending new recent original content, and the recommendation rule is used to ensure that new recent original content will definitely be recommended to the user.
第一示例，推荐规则为流量衰减规则，流量衰减规则是指为新的近期原创内容设置基础流量，每次新的近期原创内容被曝光一次当前剩余流量等于基础流量减一，当前剩余流量为0时，不再投放曝光该新的近期原创内容。流量衰减规则更适用于用户主动获取推荐内容的场景。In the first example, the recommendation rule is a traffic decay rule. The traffic decay rule means setting a basic traffic for new recent original content. Each time a new recent original content is exposed, the current remaining traffic is equal to the basic traffic minus one. When the current remaining traffic is 0, the new recent original content will no longer be exposed. The traffic decay rule is more suitable for scenarios where users actively obtain recommended content.
第二示例，推荐规则为是推送次数规则，推荐次数规则是指按照设定的推荐次数推荐该新的近期原创内容，无论新的近期原创内容是否被曝光，只要被推荐一次则剩余推荐次数减一，当剩余推荐次数为0时不再推荐。推荐次数规则更适用于用户被动获取推荐内容的场景。In the second example, the recommendation rule is the push count rule. The recommendation count rule means that the new recent original content is recommended according to the set recommendation count. Regardless of whether the new recent original content is exposed, as long as it is recommended once, the remaining recommendation count will be reduced by one. When the remaining recommendation count is 0, it will no longer be recommended. The recommendation count rule is more suitable for scenarios where users passively obtain recommended content.
在本公开实施例中，为了及时对多个第一内容池中的近期原创内容进行更新，以将用户新发布的近期原创内容推荐给用户，可将新发布的近期原创内容按照发布时间的先后顺序存储在第一资源库中。然后，可周期性地对第一内容池进行检测，当需要补充新的近期原创内容时，从第一资源库中获取最早发布的近期原创内容，将该最早发布的近期原创内容加入到第一内容池中，并从第一资源库中移除该最早发布的近期原创内容，或者标记出该最早发布的近期原创内容已被加入到第一内容池中，避免以后重复将该最早发布的近期原创内容加入到第一内容池中。In the disclosed embodiment, in order to timely update the recent original content in multiple first content pools and recommend the users' newly released recent original content to the users, the newly released recent original content can be stored in the first resource library in the order of release time. Then, the first content pool can be checked periodically, and when new recent original content needs to be added, the earliest released recent original content is obtained from the first resource library, and the earliest released recent original content is added to the first content pool, and the earliest released recent original content is removed from the first resource library, or it is marked that the earliest released recent original content has been added to the first content pool to avoid duplication in the future. The earliest published recent original content is added to the first content pool.
这里，当将新的近期原创内容加入到多个第一内容池中时，可能是只加入到了多个第一内容池中的一个第一内容池中，也可能是加入到了多个第一内容池中，这取决于加入的新的近期原创内容的用户喜爱度的高低。Here, when new recent original content is added to multiple first content pools, it may be added to only one of the multiple first content pools, or it may be added to multiple first content pools, depending on the user's preference for the added new recent original content.
另外，在多个第一内容池加入新的近期原创内容且未进行多个第一内容池更新的情况下，第一内容池D中存储的近期原创内容的用户喜爱度区间可能与其他第一内容池中存储的近期原创内容的用户喜爱度区间有重叠，但在多个第一内容池更新之后则不同第一内容池对应的用户喜爱度区间不会重叠。其中，多个第一内容池更新是指利用新加入到第一内容池中的新的近期原创内容对第一内容池中存储的近期原创内容进行更新，以将第一内容池中存储的近期劣质内容移除。In addition, when multiple first content pools are added with new recent original content and multiple first content pools are not updated, the user preference intervals of the recent original content stored in the first content pool D may overlap with the user preference intervals of the recent original content stored in other first content pools, but after the multiple first content pools are updated, the user preference intervals corresponding to different first content pools will not overlap. Among them, the update of multiple first content pools refers to updating the recent original content stored in the first content pool with the new recent original content newly added to the first content pool, so as to remove the recent low-quality content stored in the first content pool.
在本公开实施例中，多个第一内容池包括第一主动内容池D及多个第一被动内容池，多个第一被动内容池分别为第一被动内容池C、第一被动内容池B、第一被动内容池A及第一被动内容池S。在第一内容池中移除近期劣质内容后，新的近期原创内容会加入到第一内容池中，新加入的近期原创内容首先进入第一主动内容池，然后进入多个第一被动内容池。下面结合图2来介绍多个第一内容池中近期原创内容的更新过程。其中，主动内容池是指主动获取原创内容的内容池。被动内容池是指被动获取原创内容的内容池。In the disclosed embodiment, the plurality of first content pools include a first active content pool D and a plurality of first passive content pools, and the plurality of first passive content pools are respectively a first passive content pool C, a first passive content pool B, a first passive content pool A, and a first passive content pool S. After removing recent low-quality content from the first content pool, new recent original content will be added to the first content pool, and the newly added recent original content will first enter the first active content pool, and then enter the plurality of first passive content pools. The updating process of recent original content in the plurality of first content pools will be described below in conjunction with FIG. 2. Among them, the active content pool refers to a content pool that actively obtains original content. The passive content pool refers to a content pool that passively obtains original content.
图2示出了本公开实施例所提供的多个第一内容池中近期原创内容更新过程的示意图。FIG. 2 is a schematic diagram showing a recent original content update process in a plurality of first content pools provided by an embodiment of the present disclosure.
如图2所示，在本示例中，至少一个第一目标内容池包括第一主动内容池D，获取新的近期发布原创内容补充至多个第一内容池中的至少一个目标第一内容池，包括：当第一主动内容池D中的近期原创内容的数量小于第一主动内容池D对应的内容池数量阈值时，获取新的近期原创内容加入到第一主动内容池D中，以使第一主动内容池D中的近期原创内容的数量等于第一主动内容池D对应的内容池数量阈值。As shown in Figure 2, in this example, at least one first target content pool includes a first active content pool D, and new recently released original content is obtained to supplement at least one target first content pool among multiple first content pools, including: when the number of recent original content in the first active content pool D is less than the content pool quantity threshold corresponding to the first active content pool D, new recent original content is obtained and added to the first active content pool D, so that the number of recent original content in the first active content pool D is equal to the content pool quantity threshold corresponding to the first active content pool D.
为了及时对第一主动内容池D进行补充，每隔30秒检测一次第一主动内容池D中的近期原创内容的数量是否小于第一主动内容池D的内容池数量阈值1600，如果第一主动内容池D中的近期原创内容的数量小于1600，则对第一资源库进行扫描，获取新的近期原创内容，获取新的近期原创内容的数量等于内容池数量阈值1600与第一主动内容池中存储的近期原创内容的当前数量的差值，以使第一主动内容池D中近期原创内容的数量等于1600。其中，检测第一主动内容池D的时间及第一主动内容池D的内容池数量阈值是设定数值，本领域技术人员可以根据实际情况选择检测时间及内容池数量阈值的具体取值，本公开在此不作限定。In order to replenish the first active content pool D in time, it is detected every 30 seconds whether the number of recent original content in the first active content pool D is less than the content pool number threshold of 1600 of the first active content pool D. If the number of recent original content in the first active content pool D is less than 1600, the first resource library is scanned to obtain new recent original content. The number of new recent original content obtained is equal to the difference between the content pool number threshold of 1600 and the current number of recent original content stored in the first active content pool, so that the number of recent original content in the first active content pool D is equal to 1600. Among them, the time for detecting the first active content pool D and the content pool number threshold of the first active content pool D are set values. Those skilled in the art can select the specific values of the detection time and the content pool number threshold according to actual conditions, and the present disclosure does not limit them here.
在一可选实施例中，在获取新的近期原创内容加入到第一主动内容池D中之后，包括：步骤a1至步骤a3。In an optional embodiment, after acquiring new recent original content and adding it to the first active content pool D, the following steps are included: step a1 to step a3.
步骤a1，为新的近期原创内容设置基础流量，并将新的近期原创内容设置为曝光状态。Step a1, setting basic traffic for new recent original content, and setting the new recent original content to an exposure state.
为了使第一主动内容池D中的新的近期原创内容能够被有效推荐给用户，为每个加入到第一主动内容池D中的新的近期原创内容均设置了基础流量，例如：每个新的近期原创内容设置的基础流量均为500，设置基础流量后，将每个新的近期原创内容设置为曝光状态，将处于曝光状态的原创内容推荐给用户。In order to enable the new recent original content in the first active content pool D to be effectively recommended to users, a basic flow is set for each new recent original content added to the first active content pool D. For example, the basic flow set for each new recent original content is 500. After setting the basic flow, each new recent original content is set to an exposure state, and the original content in the exposure state is recommended to users.
步骤a2，当新的近期原创内容被用户浏览时，将处于曝光状态的新的近期原创内容的基础流量减一。Step a2: when new recent original content is browsed by a user, the basic flow of the new recent original content in the exposure state is reduced by one.
以新的近期原创内容A为例，当将近期原创内容A推荐给用户后，如果有用户在推荐页面中浏览了该近期原创内容的时间大于或者等于1秒，则确定为曝光一次，此时，将处于曝光状态的近期原创内容A的基础流量减一。Taking the new recent original content A as an example, after the recent original content A is recommended to the user, if a user browses the recent original content on the recommendation page for a time greater than or equal to 1 second, it is determined to be exposed once. At this time, the basic traffic of the recent original content A in the exposed state will be reduced by one.
步骤a3，当新的近期原创内容的基础流量降低至0时，将新的近期原创内容的状态由曝光状态改为等待状态，处于等待状态的近期原创内容不会被推荐给用户。Step a3: when the basic flow of new recent original content is reduced to 0, the state of the new recent original content is changed from the exposure state to the waiting state, and the recent original content in the waiting state will not be recommended to the user.
当近期原创内容A被浏览500次后，近期原创内容A的基础流量降低至0，此时，将该近期原创内容A的状态由曝光状态变更为等待状态，处于等待状态的近期原创内容A将不会被推荐给用户。When recent original content A is viewed 500 times, the basic traffic of recent original content A is reduced to 0. At this time, the status of the recent original content A is changed from the exposure status to the waiting status. The recent original content A in the waiting status will not be recommended to the user.
其中，设置等待期的目的是因为不同的游戏关卡的游玩时长不同，某些游戏关卡的游玩时长可能较长，且只有在游玩结束之后才会上传反馈信息，因此，需要设置等待期以尽可能完整地收集到玩家的反馈信息。在一示例中等待期的时长为1小时，即在1小时内对于近期原创内容A的游玩数、点赞数、收藏数和分享数都会被累加记录到近期原创内容A上。The purpose of setting a waiting period is that different game levels have different play times, and some game levels may take a long time to play, and feedback information will only be uploaded after the game is over. Therefore, a waiting period needs to be set to collect player feedback information as completely as possible. In one example, the waiting period is 1 hour, that is, the number of plays, likes, favorites, and shares of recent original content A within 1 hour will be accumulated and recorded on recent original content A.
在一可选实施例中，步骤S102，包括：步骤b1至步骤b4。In an optional embodiment, step S102 includes: step b1 to step b4.
步骤b1，确定第一主动内容池中处于排名状态的近期原创内容的数量。Step b1, determining the number of recent original contents in a ranked state in the first active content pool.
当原创内容由曝光状态变更为等待状态之后，计算近期原创内容进入等待状态的累计时长，当累计时长达到等待时长阈值时等待状态结束，将新的近期原创内容由等待状态改为排名状态。When original content changes from exposure status to waiting status, the cumulative time that recent original content has been in waiting status is calculated. When the cumulative time reaches the waiting time threshold, the waiting status ends and the new recent original content is changed from waiting status to ranking status.
每当一个原创内容由等待状态进入排名状态后，就会统计第一主动内容池中处于排名状态的近期原创内容的数量，以根据该数量确定是否开始对处于排名状态的原创内容进行喜爱度评分。为保证公平性，当原创内容进入排名状态时，将不再进行反馈信息收集。Whenever an original content enters the ranking state from the waiting state, the number of recent original content in the ranking state in the first active content pool will be counted to determine whether to start liking the original content in the ranking state. To ensure fairness, when the original content enters the ranking state, feedback information will no longer be collected.
步骤b2，当处于排名状态的近期原创内容的数量达到第一评分数量阈值时，确定处于排名状态的近期原创内容的喜爱度评分。Step b2: when the number of recent original contents in the ranking state reaches a first rating number threshold, determine the popularity rating of the recent original contents in the ranking state.
当第一主动内容池D中处于排名状态的原创内容的数量达到第一评分数量阈值100时，就会进行喜爱度评分计算，计算每一个处于排名状态的近期原创内容的喜爱度评分。其中，喜爱度评分由用户针对原创内容的反馈信息决定，例如：分享数及点赞数越多，则喜爱度评分越高；分享数及点赞数越少，则喜爱度评分越低。When the number of original content in the ranking state in the first active content pool D reaches the first rating threshold of 100, the popularity rating calculation will be performed to calculate the popularity rating of each recent original content in the ranking state. The popularity rating is determined by the user's feedback information on the original content. For example, the more shares and likes, the higher the popularity rating; the fewer shares and likes, the lower the popularity rating.
步骤b3，将第一主动内容池中喜爱度评分排名靠前的第一设定数量的近期原创内容作为近期优质内容，将近期优质内容转移到多个第一被动内容池中。Step b3: taking a first set number of recent original contents with high popularity scores in the first active content pool as recent high-quality contents, and transferring the recent high-quality contents to a plurality of first passive content pools.
将喜爱度评分较高的原创内容升级到更高级的第一内容池中，即将喜爱度评分较高的前M个原创内容作为近期优质内容，将M个近期优质内容放入第一被动内容池C中，并将M个近期优质内容从第一主动内容池中D移除。Upgrade the original contents with higher likeability scores to the more advanced first content pool, that is, take the first M original contents with higher likeability scores as recent high-quality contents, put the M recent high-quality contents into the first passive content pool C, and remove the M recent high-quality contents from the first active content pool D.
其中，多个第一内容池按照级别由低到高依次为第一主动内容池D、第一被动内容池C、第一被动内容池B、第一被动内容池A、第一被动内容池S，即第一主动内容池D是级别最低的第一内容池，第一被动内容池的级别均高于第一主动内容池。第一内容池的级别与该内容池中原创内容的喜爱度评分高低相对应，原创内容的喜爱度评分越高，则第一内容池的级别越高；原创内容的喜爱度评分越低，则第一内容池的级别越低。Among them, the multiple first content pools are first active content pool D, first passive content pool C, first passive content pool B, first passive content pool A, and first passive content pool S in order of level from low to high, that is, first active content pool D is the first content pool with the lowest level, and the levels of first passive content pools are all higher than those of first active content pools. The level of the first content pool corresponds to the popularity score of the original content in the content pool. The higher the popularity score of the original content, the higher the level of the first content pool; the lower the popularity score of the original content, the lower the level of the first content pool.
步骤b4，利用近期优质内容，将近期劣质内容从多个第一内容池中移除。Step b4, using recent high-quality content to remove recent low-quality content from multiple first content pools.
将M个近期优质内容放入第一被动内容池中，并将M个近期优质内容从第一主动内容池中移除之后，还要将第一主动内容池D中剩余的原创内容删除，完成对第一主动内容池D的一轮更新。同时，还要确定多个第一被动内容池中的近期劣质内容，以将近期劣质内容从多个第一被动内容池中移除。After placing M recent high-quality contents into the first passive content pool and removing M recent high-quality contents from the first active content pool, the remaining original contents in the first active content pool D are deleted to complete a round of updating of the first active content pool D. At the same time, recent low-quality contents in multiple first passive content pools are determined to remove the recent low-quality contents from the multiple first passive content pools.
在一可选实施例中，多个第一被动内容池按照各自存储的近期原创内容的推荐程度由高到低具备对应的级别，利用近期优质内容，将近期劣质内容从多个第一内容池中移除，包括：步骤c1至步骤c3。In an optional embodiment, multiple first passive content pools have corresponding levels from high to low according to the recommendation degree of recent original content stored in each of them, and recent low-quality content is removed from the multiple first content pools by using recent high-quality content, including: steps c1 to c3.
这里，由于加入到第一被动内容池C中的新的近期原创内容的喜爱度评分可能高于其他第一被动内容池中的近期原创内容的喜爱度评分，为此通过递进升级的方式来确定新加入的近期原创内容应该被存放入哪个第一被动内容池中。Here, since the popularity score of the new recent original content added to the first passive content pool C may be higher than the popularity score of the recent original content in other first passive content pools, a progressive upgrade method is used to determine which first passive content pool the newly added recent original content should be stored in.
由于近期原创内容存在递进升级的过程，每个第一内容池中喜爱度评分范围不固定，为了便于说明第一内容池的级别，可根据第一内容池中存放的近期原创内容的推荐程度来确定第一内容池的级别，推荐程度越高则级别越高。总体喜爱度评分越高，则推荐程度越高；总体喜爱度评分越低，则推荐程度越低。Since recent original content has a progressive upgrade process, the popularity score range in each first content pool is not fixed. In order to facilitate the description of the level of the first content pool, the level of the first content pool can be determined according to the recommendation level of the recent original content stored in the first content pool. The higher the recommendation level, the higher the level. The higher the overall popularity score, the higher the recommendation level; the lower the overall popularity score, the lower the recommendation level.
步骤c1，将第一设定数量的近期优质内容加入到最低级别的第一被动内容池中。Step c1: adding a first set number of recent high-quality content to a first passive content pool of the lowest level.
以上述示例为例，如果是将M个近期优质内容放入多个第一被动内容池中，则第一设定数量即为M。当将M个近期优质内容放入多个第一被动内容池中时，是先将这M个近期优质内容放入到第一被动内容池C中，即先放入最低级别的第一被动内容池中，因为，最低级别的第一被动内容池中存放的是所有第一被动内容池中喜爱度评分最低档次的近期原创内容。Taking the above example, if M recent high-quality contents are placed in multiple first passive content pools, the first set number is M. When M recent high-quality contents are placed in multiple first passive content pools, the M recent high-quality contents are first placed in the first passive content pool C, that is, the first passive content pool of the lowest level, because the first passive content pool of the lowest level stores the recent original contents with the lowest popularity rating among all the first passive content pools.
步骤c2，由加入第一设定数量的近期优质内容的最低级别的第一被动内容池开始，递进地将每个第一被动内容池中的近期优质内容升级至高一级别的第一被动内容池，直至将次高级别的第一被动内容池中的近期优质内容升级至最高级别的第一被动内容池。Step c2, starting from the lowest-level first passive content pool to which a first set number of recent high-quality contents are added, the recent high-quality contents in each first passive content pool are progressively upgraded to a first passive content pool of a higher level, until the recent high-quality contents in the second-highest-level first passive content pool are upgraded to the highest-level first passive content pool.
将M个近期优质内容与第一被动内容池C中已存放的近期原创内容进行比较，确定哪些第一被动内容池C中的近期优质内容可以升级至第一被动内容池B中，将从第一被动内容池C中选取的可以升级至第一被动内容池B中的近期优质内容称为第一近期优质内容。Compare M recent high-quality contents with the recent original contents stored in the first passive content pool C to determine which recent high-quality contents in the first passive content pool C can be upgraded to the first passive content pool B, and the recent high-quality contents selected from the first passive content pool C that can be upgraded to the first passive content pool B are called the first recent high-quality contents.
然后，将升入第一被动内容池B中的第一近期优质内容与第一被动内容池B中已存放的近期原创内容进行比较，确定哪些第一被动内容池B中的近期优质内容可以升级至第一被动内容池A中，将从第一被动内容池B中选取的可以升级至第一被动内容池A中的近期优质内容称为第二近期优质内容。Then, the first recent high-quality content upgraded to the first passive content pool B is compared with the recent original content stored in the first passive content pool B to determine which recent high-quality content in the first passive content pool B can be upgraded to the first passive content pool A, and the recent high-quality content selected from the first passive content pool B that can be upgraded to the first passive content pool A is called the second recent high-quality content.
以此类推，直至将第一被动内容池A中的第三近期优质内容升级至第一被动内容池S中。And so on, until the third recent high-quality content in the first passive content pool A is upgraded to the first passive content pool S.
步骤c3，由加入近期优质内容的最高级别的第一被动内容池开始，递进地将每个第一被动内容池中的近期劣质内容降级至低一级别的第一被动内容池，直至将第一设定数量的近期劣质内容从最低级别的第一被动内容池中移除。Step c3, starting from the highest level first passive content pool to which recent high-quality content is added, progressively downgrading recent low-quality content in each first passive content pool to a first passive content pool of a lower level, until a first set number of recent low-quality content is removed from the lowest level first passive content pool.
确定第一被动内容池S中的第一近期劣质内容，将第一近期劣质内容转移至第一被动内容池A中，确定哪些第一被动内容池A中的近期劣质内容可以降级至第一被动内容池B中，将从第一被动内容池A中选取的可以降级至第一被动内容池B中的近期劣质内容称为第二近期劣质内容。Determine the first recent low-quality content in the first passive content pool S, transfer the first recent low-quality content to the first passive content pool A, determine which recent low-quality content in the first passive content pool A can be downgraded to the first passive content pool B, and transfer the first recent low-quality content from the first passive content pool A to the first passive content pool B. The recent low-quality content selected from A that can be downgraded to the first passive content pool B is called the second recent low-quality content.
然后，确定哪些第一被动内容池B中的近期劣质内容可以降级至第一被动内容池C中，将从第一被动内容池B中选取的可以降级至第一被动内容池C中的近期劣质内容称为第三近期优质内容。Then, it is determined which recent low-quality contents in the first passive content pool B can be downgraded to the first passive content pool C, and the recent low-quality contents selected from the first passive content pool B that can be downgraded to the first passive content pool C are called third recent high-quality contents.
以此类推，直至将第一被动内容池C中的第四近期劣质内容被从第一被动内容池C中移除。在一轮升降级过程中，单个近期优质内容只能升级一次，单个近期劣质内容只能降级一次，避免单个近期优质内容在一轮中连续升级及单个劣质内容在一轮中连续降级的情况出现。And so on, until the fourth recent low-quality content in the first passive content pool C is removed from the first passive content pool C. In a round of upgrade and downgrade, a single recent high-quality content can only be upgraded once, and a single recent low-quality content can only be downgraded once, to avoid the situation where a single recent high-quality content is continuously upgraded in a round and a single low-quality content is continuously downgraded in a round.
在一可选实施例中，步骤c2，包括：步骤c21至步骤c24。In an optional embodiment, step c2 includes: steps c21 to c24.
步骤c21，选取最低级别的第一被动内容池作为第一升级被动内容池。Step c21: Select the first passive content pool of the lowest level as the first upgraded passive content pool.
在执行近期优质内容升级时，首先将第一被动内容池C作为第一升级被动内容池。When performing recent high-quality content upgrade, the first passive content pool C is first used as the first upgraded passive content pool.
步骤c22，确定第一升级被动内容池是否为最高级别的第一被动内容池。Step c22: determining whether the first upgraded passive content pool is the first passive content pool of the highest level.
步骤c23，若第一升级被动内容池不是最高级别的第一被动内容池，从第一升级被动内容池中的近期原创内容中选取近期优质内容，将近期优质内容转移至比第一升级被动内容池的高一级别的第一被动内容池中。Step c23, if the first upgraded passive content pool is not the first passive content pool of the highest level, select recent high-quality content from the recent original content in the first upgraded passive content pool, and transfer the recent high-quality content to the first passive content pool of a higher level than the first upgraded passive content pool.
由于第一被动内容池C不是最高级别的第一被动内容池，在满足条件时从第一被动内容池C的近期原创内容选取第一近期候选优质内容，从第一近期候选优质内容中选取第一近期优质内容，然后将第一近期优质内容从第一被动内容池C中转移至第一被动内容池B中。Since the first passive content pool C is not the highest-level first passive content pool, when the conditions are met, the first recent candidate high-quality content is selected from the recent original content of the first passive content pool C, the first recent high-quality content is selected from the first recent candidate high-quality content, and then the first recent high-quality content is transferred from the first passive content pool C to the first passive content pool B.
如果第一被动内容池是最高级别的第一被动内容池，则近期优质内容升级过程结束，开始执行近期劣质内容降级过程。If the first passive content pool is the first passive content pool of the highest level, the recent high-quality content upgrade process ends, and the recent low-quality content downgrade process begins.
步骤c24，将高一级别的第一被动内容池作为新的第一升级被动内容池，返回执行从第一升级被动内容池中的近期原创内容中选取近期优质内容的步骤。Step c24, taking the higher-level first passive content pool as a new first upgraded passive content pool, and returning to execute the step of selecting recent high-quality content from the recent original content in the first upgraded passive content pool.
将第一近期优质内容转移至第一被动内容池B后，将第一被动内容池B作为新的第一升级被动内容池，继续从第一被动内容池B中选取第二近期优质内容。After the first recent high-quality content is transferred to the first passive content pool B, the first passive content pool B is used as a new first upgraded passive content pool, and the second recent high-quality content continues to be selected from the first passive content pool B.
将第一近期优质内容转移至第一被动内容池B后，在满足条件时从第一被动内容池B的近期原创内容中选取第二近期候选优质内容，从第二近期候选优质内容中选取第二近期优质内容，将第二近期优质内容从第一被动内容池B中转移至第一被动内容池A中。After the first recent high-quality content is transferred to the first passive content pool B, when the conditions are met, the second recent candidate high-quality content is selected from the recent original content of the first passive content pool B, and the second recent high-quality content is selected from the second recent candidate high-quality content, and the second recent high-quality content is transferred from the first passive content pool B to the first passive content pool A.
然后，将第一被动内容池A作为新的第一升级被动内容池，在满足条件时从第一被动内容池A的近期原创内容中选取第三近期候选优质内容，从第三近期候选优质内容中选取第三近期优质内容，将第三近期优质内容从第一被动内容池A中转移至第一被动内容池S中。Then, the first passive content pool A is used as the new first upgraded passive content pool, and when conditions are met, the third recent candidate high-quality content is selected from the recent original content of the first passive content pool A, and the third recent high-quality content is selected from the third recent candidate high-quality content, and the third recent high-quality content is transferred from the first passive content pool A to the first passive content pool S.
由于第一被动内容池S是最高级别的第一被动内容池，则近期优质内容升级过程结束，开始执行近期劣质内容降级过程。Since the first passive content pool S is the first passive content pool of the highest level, the recent high-quality content upgrade process ends, and the recent low-quality content downgrade process begins.
在一可选实施例中，步骤c3，包括：步骤c31至步骤c34。In an optional embodiment, step c3 includes: steps c31 to c34.
步骤c31，选取最高级别的第一被动内容池作为第一降级被动内容池。Step c31: Select the first passive content pool with the highest level as the first degraded passive content pool.
在执行近期劣质内容降级时，首先将第一被动内容池S作为第一降级被动内容池。When performing recent low-quality content downgrading, the first passive content pool S is first used as the first downgraded passive content pool.
步骤c32，确定第一降级被动内容池是否为最低级别的第一被动内容池。Step c32: determining whether the first downgraded passive content pool is the first passive content pool of the lowest level.
步骤c33，若第一降级被动内容池不是最低级别的第一被动内容池，从第一降级被动内容池中的近期原创内容中选取近期劣质内容，将近期劣质内容转移至第一降级被动内容池的低一级别的第一被动内容池中。Step c33: if the first degraded passive content pool is not the first passive content pool of the lowest level, select recent low-quality content from the recent original content in the first degraded passive content pool, and transfer the recent low-quality content to the first passive content pool of the lower level of the first degraded passive content pool.
第一被动内容池S接受由第一被动内容池A升级来的第三近期优质内容，由于第一被动内容池S不是最低级别的第一被动内容池，在满足条件时从第一被动内容池S的近期原创内容中选取第一近期候选劣质内容，从第一近期候选劣质内容中选取第一近期劣质内容，将第一近期劣质内容从第一被动内容池S中降级至第一被动内容池A中。The first passive content pool S accepts the third recent high-quality content upgraded from the first passive content pool A. Since the first passive content pool S is not the lowest-level first passive content pool, when the conditions are met, the first recent candidate low-quality content is selected from the recent original content of the first passive content pool S, and the first recent low-quality content is selected from the first recent candidate low-quality content, and the first recent low-quality content is downgraded from the first passive content pool S to the first passive content pool A.
步骤c34，将低一级别的第一被动内容池作为新的第一降级被动内容池，返回执行从第一降级被动内容池中的近期原创内容中选取近期劣质内容的步骤。Step c34: Use the first passive content pool at a lower level as a new first degraded passive content pool, and return to the step of selecting recent low-quality content from the recent original content in the first degraded passive content pool.
将第一被动内容池A作为新的第一降级被动内容池，在满足条件时从第一被动内容池A的近期原创内容中选取第二近期候选劣质内容，从第二近期候选劣质内容中选取第二近期劣质内容，将第二近期劣质内容从第一被动内容池A转移至第一被动内容池B中。The first passive content pool A is used as the new first downgraded passive content pool. When conditions are met, the second recent candidate low-quality content is selected from the recent original content of the first passive content pool A, and the second recent low-quality content is selected from the second recent candidate low-quality content, and the second recent low-quality content is transferred from the first passive content pool A to the first passive content pool B.
将第一被动内容池B作为新的第一降级被动内容池，在满足条件时从第一被动内容池B的近期原创内容中选取第三近期候选劣质内容，从第三近期候选劣质内容中选取第三近期劣质内容，将第三近期劣质内容从第一被动内容池B转移至第一被动内容池C中。The first passive content pool B is used as the new first downgraded passive content pool. When conditions are met, the third recent candidate low-quality content is selected from the recent original content of the first passive content pool B, and the third recent low-quality content is selected from the third recent candidate low-quality content. The third recent low-quality content is transferred from the first passive content pool B to the first passive content pool C.
将第一被动内容池C作为新的第一降级被动内容池，由于第一被动内容池C是最低级别的第一被动内容池，则在满足条件时从第一被动内容池C的近期原创内容中选取第四近期候选劣质内容，从第四近期候选劣质内容中选取第四近期劣质内容，将第四近期劣质内容从第一被动内容池C中移除，完成一轮近期劣质内容的移除过程。The first passive content pool C is used as a new first downgraded passive content pool. Since the first passive content pool C is the lowest level first passive content pool, when the conditions are met, the fourth recent candidate low-quality content is selected from the recent original content of the first passive content pool C, the fourth recent low-quality content is selected from the fourth recent candidate low-quality content, and the fourth recent low-quality content is removed from the first passive content pool C. This is a process of removing recent low-quality content.
在一可选实施例中，步骤c23从第一升级被动内容池中的近期原创内容中选取近期优质内容，包括：步骤c231至步骤c233。In an optional embodiment, step c23 selects recent high-quality content from recent original content in the first upgraded passive content pool, including: steps c231 to c233.
步骤c231，在满足评比条件时，按照近期原创内容进入第一升级被动内容池的时间先后顺序进行排序，选取排名靠前的固定比例的近期原创内容作为近期候选优质内容。Step c231, when the evaluation conditions are met, the recent original content is sorted in the order of time when it enters the first upgraded passive content pool, and a fixed proportion of the recent original content with high rankings is selected as the recent candidate high-quality content.
评比条件可指对喜爱度进行评分的条件。在本公开实施例中，评比条件为第一升级被动内容池中的近期原创内容达到对应的内容池数量阈值。The evaluation condition may refer to a condition for scoring the likeability. In the embodiment of the present disclosure, the evaluation condition is that the recent original content in the first upgraded passive content pool reaches the corresponding content pool quantity threshold.
以第一升级被动内容池为第一被动内容池C为例，记录新的近期原创内容进入第一被动内容池C的时间，当第一被动内容池C中的近期原创内容达到第一被动内容池C的内容池数量阈值时，按照进入内容池的时间先后顺序，对所有已存入第一被动内容池C的近期原创内容进行排序。选取排名前50％的近期原创内容作为第一近期候选优质内容。Taking the first upgraded passive content pool as the first passive content pool C as an example, the time when the new recent original content enters the first passive content pool C is recorded. When the recent original content in the first passive content pool C reaches the content pool quantity threshold of the first passive content pool C, all recent original content stored in the first passive content pool C are sorted according to the time sequence of entering the content pool. The top 50% of recent original content is selected as the first recent candidate high-quality content.
以第一升级被动内容池为第一被动内容池B为例，将第一近期优质内容转移至第一被动内容池B后，记录第一近期优质内容进入第一被动内容池B的时间，当第一被动内容池B中的近期原创内容达到第一被动内容池B的内容池数量阈值时，按照进入内容池的时间先后顺序，对所有已存入第一被动内容池B的近期原创内容进行排序，选取排名前50％的近期原创内容作为第二近期候选优质内容。Taking the first upgraded passive content pool as the first passive content pool B as an example, after the first recent high-quality content is transferred to the first passive content pool B, the time when the first recent high-quality content enters the first passive content pool B is recorded. When the recent original content in the first passive content pool B reaches the content pool quantity threshold of the first passive content pool B, all recent original content stored in the first passive content pool B are sorted according to the time order of entering the content pool, and the top 50% of recent original content are selected as the second recent candidate high-quality content.
步骤c232，确定近期候选优质内容的当前喜爱度评分。Step c232, determining the current popularity score of the recent candidate high-quality content.
获取近期候选优质内容最新的反馈信息，根据最新的反馈信息及喜爱度评分公式，计算近期候选优质内容的当前喜爱度评分。The latest feedback information of recent candidate high-quality content is obtained, and the current popularity score of recent candidate high-quality content is calculated based on the latest feedback information and the popularity score formula.
步骤c233，按照当前喜爱度评分由高到低的顺序对近期候选优质内容进行排序，选取排名靠前的第一设定数量中与第一升级被动内容池对应的预设升级数量的近期候选优质内容作为最终的近期优质内容。Step c233, sorting the recent high-quality content candidates in descending order of current popularity scores, and selecting the preset upgraded number of recent high-quality content candidates corresponding to the first upgraded passive content pool from the first set number of top rankings as the final recent high-quality content.
每个第一被动内容池对应的预设升级数量是不同的，以第一设定数量为M为例，则第一被动内容池C对应的预设升级数量为M/2，第一被动内容池B对应的预设升级数量为M/4，第一被动内容池A对应的预设升级数量为M/8。The preset upgrade quantity corresponding to each first passive content pool is different. Taking the first set quantity as M as an example, the preset upgrade quantity corresponding to the first passive content pool C is M/2, the preset upgrade quantity corresponding to the first passive content pool B is M/4, and the preset upgrade quantity corresponding to the first passive content pool A is M/8.
以第一升级被动内容池为第一被动内容池C为例，如果共有16个新的近期原创内容加入到第一被动内容池C中，则会从第一被动内容池C中选取8个第一近期优质内容转移至第一被动内容池B中，这8个第一近期优质内容就是选取排名靠前的与第一被动内容池C对应的预设升级数量的近期候选优质内容，也是从第一被动内容池C中选取的最终的近期优质内容。Taking the first upgraded passive content pool as the first passive content pool C as an example, if a total of 16 new recent original content are added to the first passive content pool C, 8 first recent high-quality content will be selected from the first passive content pool C and transferred to the first passive content pool B. These 8 first recent high-quality content are the top-ranked recent candidate high-quality content corresponding to the preset upgraded number of the first passive content pool C, and are also the final recent high-quality content selected from the first passive content pool C.
在一可选实施例中，步骤c33从第一降级被动内容池中的近期原创内容中选取近期劣质内容，包括：步骤c331至步骤c333。In an optional embodiment, step c33 selects recent low-quality content from recent original content in the first degraded passive content pool, including: steps c331 to c333.
步骤c331，在满足评比条件时，按照近期原创内容进入第一降级被动内容池的时间先后顺序进行排序，选取排名靠前的固定比例的近期原创内容作为近期候选劣质内容。Step c331, when the evaluation conditions are met, recent original content is sorted in the order of time when it enters the first downgraded passive content pool, and a fixed proportion of recent original content with a high ranking is selected as recent candidate low-quality content.
评比条件可指对喜爱度进行评分的条件。在本公开实施例中，评比条件为第一降级被动内容池中的近期原创内容达到对应的内容池数量阈值。The evaluation condition may refer to a condition for scoring the likeability. In the embodiment of the present disclosure, the evaluation condition is that the recent original content in the first downgraded passive content pool reaches the corresponding content pool quantity threshold.
以第一降级被动内容池为第一被动内容池A为例，第一被动内容池A接受第一被动内容池S降级的M/8个第一近期劣质内容，记录第一近期劣质内容进入第一被动内容池A的时间。当第一被动内容池A中的近期原创内容达到第一被动内容池A的内容池数量阈值时，按照进入内容池的时间先后顺序，对所有已存入第一被动内容池A的近期原创内容进行排序。选取排名前50％的近期原创内容作为第二近期候选劣质内容。Taking the first downgraded passive content pool as the first passive content pool A as an example, the first passive content pool A accepts M/8 first recent low-quality contents downgraded by the first passive content pool S, and records the time when the first recent low-quality contents enter the first passive content pool A. When the recent original contents in the first passive content pool A reach the content pool quantity threshold of the first passive content pool A, all recent original contents stored in the first passive content pool A are sorted according to the time sequence of entering the content pool. The top 50% of recent original contents are selected as the second recent candidate low-quality contents.
以第一降级被动内容池为第一被动内容池B为例，第一被动内容池B接受第一被动内容池A降级的M/4个第二近期劣质内容，记录第二近期优质内容进入第一被动内容池B的时间，当第一被动内容池B中的近期原创内容达到第一被动内容池B的内容池数量阈值时，按照进入内容池的时间先后顺序，对所有已存入第一被动内容池B的近期原创内容进行排序，选取排名前50％的近期原创内容作为第二近期候选劣质内容。Taking the first downgraded passive content pool as the first passive content pool B as an example, the first passive content pool B accepts M/4 second recent low-quality content downgraded from the first passive content pool A, and records the time when the second recent high-quality content enters the first passive content pool B. When the recent original content in the first passive content pool B reaches the content pool quantity threshold of the first passive content pool B, all recent original content stored in the first passive content pool B are sorted according to the time order of entering the content pool, and the top 50% of recent original content are selected as the second recent candidate low-quality content.
步骤c332，确定近期候选劣质内容的当前喜爱度评分。Step c332, determining the current popularity score of the recent candidate low-quality content.
获取近期候选劣质内容最新的反馈信息，根据最新的反馈信息及喜爱度评分公式，计算近期候选劣质内容的当前喜爱度评分。The latest feedback information of the recent candidate low-quality content is obtained, and the current popularity score of the recent candidate low-quality content is calculated according to the latest feedback information and the popularity score formula.
步骤c333，按照当前喜爱度评分由高到低的顺序对近期候选劣质内容进行排序，选取排名靠后的第一设定数量中与第一降级被动内容池对应的预设降级数量的近期候选劣质内容作为最终的近期劣质内容。Step c333, sorting the recent low-quality candidate contents in descending order of current popularity scores, and selecting a preset number of recent low-quality candidate contents from a first set number of low-ranking recent contents corresponding to the first downgraded passive content pool as the final recent low-quality contents.
每个第一被动内容池对应的预设降级数量是不同的，以第一设定数量为M为例，则第一被动内容池S对应的预设降级数量为M/8，第一被动内容池A对应的预设降级数量为M/4，第一被动内容池B对应的预设降级数量为M/2，第一被动内容池C对应的预设降级数量为M。The preset downgrade quantity corresponding to each first passive content pool is different. Taking the first set quantity as M as an example, the preset downgrade quantity corresponding to the first passive content pool S is M/8, the preset downgrade quantity corresponding to the first passive content pool A is M/4, and the preset downgrade quantity corresponding to the first passive content pool B is M/8. The corresponding preset downgrade quantity is M/2, and the preset downgrade quantity corresponding to the first passive content pool C is M.
以第一降级被动内容池为第一被动内容池C为例，如果共有16个新的近期原创内容加入到第一被动内容池C中，则会选取16个第四近期劣质内容从第一被动内容池C中删除。Taking the first downgraded passive content pool as the first passive content pool C as an example, if a total of 16 new recent original contents are added to the first passive content pool C, 16 fourth recent low-quality contents will be selected and deleted from the first passive content pool C.
可见，如果将所有的第一被动内容池看成一个整体，则在第一被动内容池组合中，每输入M个近期原创内容，就会淘汰M个近期原创内容，从而保证第一被动内容池组合的近期原创内容的数量的稳定性。It can be seen that if all the first passive content pools are regarded as a whole, then in the first passive content pool combination, every time M recent original content is input, M recent original content will be eliminated, thereby ensuring the stability of the number of recent original content in the first passive content pool combination.
在一可选实施例中，多个第一被动内容池对应的预设升级数量的数值大小随着第一被动内容池级别的增加逐渐减少。In an optional embodiment, the numerical values of the preset upgrade quantities corresponding to the plurality of first passive content pools gradually decrease as the level of the first passive content pool increases.
以上述示例为例，第一设定数量为M，则第一被动内容池C对应的预设升级数量为M/2，第一被动内容池B对应的预设升级数量为M/4，第一被动内容池A对应的预设升级数量为M/8。可见，每当第一被动内容池的级别升高一级时，预设升级数量的数值会减少一半，即随着第一被动内容池的级别的增加而逐渐减少。需要说明的是，预设升级数量减少的幅度不固定为一半，本领域技术人员可以根据实际情况选择预设升级数量的减少幅度，本公开在此不作限定。Taking the above example, the first set number is M, then the preset upgrade number corresponding to the first passive content pool C is M/2, the preset upgrade number corresponding to the first passive content pool B is M/4, and the preset upgrade number corresponding to the first passive content pool A is M/8. It can be seen that every time the level of the first passive content pool is increased by one level, the value of the preset upgrade number will be reduced by half, that is, it will gradually decrease as the level of the first passive content pool increases. It should be noted that the reduction range of the preset upgrade number is not fixed to half, and those skilled in the art can select the reduction range of the preset upgrade number according to actual conditions, and the present disclosure does not limit it here.
在一可选实施例中，多个第一被动内容池对应的预设降级数量的数值大小随着第一被动内容池级别的降低逐渐增加。In an optional embodiment, the numerical values of the preset downgrade numbers corresponding to the plurality of first passive content pools gradually increase as the level of the first passive content pool decreases.
以上述示例为例，第一设定数量为M，则第一被动内容池S对应的预设降级数量为M/8，第一被动内容池A对应的预设降级数量为M/4，第一被动内容池B对应的预设降级数量为M/2，第一被动内容池C对应的预设降级数量为M。可见，每当第一被动内容池的级别降低一级时，预设降级数量的数值会增加一倍，即随着第一被动内容池的级别的降低而逐渐增加。需要说明的是，预设降级数量增加的幅度不固定为一倍，本领域技术人员可以根据实际情况选择预设降级数量的增加幅度，本公开在此不作限定。Taking the above example, the first set number is M, then the preset downgrade number corresponding to the first passive content pool S is M/8, the preset downgrade number corresponding to the first passive content pool A is M/4, the preset downgrade number corresponding to the first passive content pool B is M/2, and the preset downgrade number corresponding to the first passive content pool C is M. It can be seen that every time the level of the first passive content pool is reduced by one level, the value of the preset downgrade number will double, that is, it will gradually increase as the level of the first passive content pool decreases. It should be noted that the increase in the preset downgrade number is not fixed to one time, and those skilled in the art can select the increase in the preset downgrade number according to actual conditions, and the present disclosure does not limit it here.
需要说明的是，同一第一被动内容池的预设升级数量与预设降级数量是相等的，并且第一被动内容池中的近期原创内容的数量是稳定的，可以在用户发布的近期原创内容数量较少、曝光数量较多的情况下，金字塔内容池不会被曝光操作消耗至空，可以保持稳定的推荐功能。It should be noted that the preset upgrade number and the preset downgrade number of the same first passive content pool are equal, and the number of recent original content in the first passive content pool is stable. When the number of recent original content posted by users is small and the number of exposures is large, the pyramid content pool will not be consumed by the exposure operation, and a stable recommendation function can be maintained.
图3示出了本公开实施例所提供的计算喜爱度评分的流程图。FIG. 3 shows a flow chart of calculating a likeability score provided by an embodiment of the present disclosure.
通过步骤S201、步骤S202、步骤S203、步骤204确定近期原创内容的喜爱度评分：Determine the popularity score of recent original content through steps S201, S202, S203, and S204:
步骤S201，根据近期原创内容的静态数据，确定静态质量评分。Step S201, determining a static quality score based on static data of recent original content.
对于用户创作的每一个近期原创内容，需要设定一个标准来评价其受欢迎程度，越获得用户喜爱的近期原创内容越应该升级到更高级的内容池中，以获得更多的曝光机会。基于此，本公开采用静态质量评分、动态质量评分、时间衰减系数三位一体的评测方法，从创作者、评测者和时间三个角度综合考量，最终得到了一个全方位的喜爱度评分计算方法。For each recent original content created by users, a standard needs to be set to evaluate its popularity. The more recent original content that is popular with users, the more it should be upgraded to a higher-level content pool to gain more exposure opportunities. Based on this, this paper adopts a three-in-one evaluation method of static quality scoring, dynamic quality scoring, and time decay coefficient, comprehensively considering the three perspectives of creators, evaluators, and time, and finally obtains a comprehensive method for calculating the popularity score.
静态质量评分主要考量了近期原创内容刚发布时所携带的静态数据，是从用户创作该近期原创内容所付出的心血的角度出发，评价出的一个数值。创作对该近期原创内容付出的越多，该近期原创内容越有可能拥有更高的质量。具体而言，考虑以下10类静态特征，如下表1所示。这些静态特征都代表创作近期原创内容时的成本。考虑到这些静态特征之间的重要度不一样，本公开利用机器学习的方法，在大量已发布近期原创内容的数据中，学习出这些静态特征和近期原创内容未来动态质量之间的关系，利用深度神经网络将这些静态特征拟合在一起，最终得到该近期原创内容对应的静态质量评分。The static quality score mainly considers the static data carried by the recent original content when it is just released. It is a value evaluated from the perspective of the effort put into creating the recent original content by the user. The more effort is put into creating the recent original content, the more likely the recent original content is to have a higher quality. Specifically, consider the following 10 categories of static features, as shown in Table 1 below. These static features all represent the cost of creating recent original content. Taking into account the different importance of these static features, the present disclosure uses machine learning methods to learn the relationship between these static features and the future dynamic quality of recent original content from a large amount of data on recently released original content, and uses deep neural networks to fit these static features together, and finally obtains the static quality score corresponding to the recent original content.
表1：静态特征数据表。
Table 1: Static characteristics data table.
如表1所示，静态特征包括制作时间、组件占用值、容量大小、描述长度、组件总数量、组件种类数量、是否是初始名称、是否包含音乐、类型及各类型组件数量。其中，类型指的是近期原创内容的类型，以近期原创内容为游戏关卡为例，则类型可以是竞速、解密等类型。组件指的是制作近期原创内容所使用的组件。As shown in Table 1, static features include production time, component occupancy value, capacity size, description length, total number of components, number of component types, whether it is the initial name, whether it contains music, type, and the number of components of each type. Among them, type refers to the type of recent original content. Taking the recent original content as a game level as an example, the type can be racing, decryption, etc. Components refer to the components used to produce recent original content.
利用深度神经网络模型对近期原创内容的十种静态特征进行评价，确定该近期原创内容的静态质量评分。A deep neural network model is used to evaluate ten static features of recent original content to determine the static quality score of the recent original content.
步骤S202，根据用户对近期原创内容的反馈信息，确定动态质量评分。Step S202: Determine a dynamic quality score based on user feedback on recent original content.
动态质量评分用于表征近期原创内容在曝光给用户一定次数后，用户对该近期原创内容反馈的好坏。用户反馈信息包括但不限于：游玩、点赞、收藏和分享。其中，游玩可指用户看到近期原创内容后是否会游玩；点赞可指用户游玩近期原创内容后是否点赞；收藏可指用户游玩近期原创内容后是否收藏到自己的收藏夹中；分享可指用户游玩近期原创内容后是否分享给好友或者其他用户。Dynamic quality ratings are used to characterize the quality of user feedback on recent original content after it has been exposed to users a certain number of times. User feedback information includes, but is not limited to: playing, liking, collecting, and sharing. Among them, playing refers to whether a user will play the recent original content after seeing it; liking refers to whether a user likes the recent original content after playing it; collecting refers to whether a user collects the recent original content into his or her own favorites after playing it; sharing refers to whether a user shares the recent original content with friends or other users after playing it.
这里，根据不同用户对近期原创内容的游玩总数、点赞总数、收藏总数及分享总数来确定一个近期原创内容的动态质量评分。由于不同的内容池拥有的曝光次数不同，需要对上述动态特征进行归一化处理，将游玩总数与曝光总数的比值作为游玩率，将点赞总数与游玩总数的比值作为点赞率，将收藏总数与游玩总数的比值作为收藏率，将分享总数与游玩总数的比值作为分享率。游玩率、点赞率、收藏率及分享率为衡量动态质量的四个动态质量评价指标。Here, the dynamic quality score of a recent original content is determined based on the total number of plays, likes, collections, and shares of the recent original content by different users. Since different content pools have different exposure times, the above dynamic features need to be normalized, and the ratio of the total number of plays to the total number of exposures is used as the play rate, the ratio of the total number of likes to the total number of plays is used as the like rate, the ratio of the total number of collections to the total number of plays is used as the collection rate, and the ratio of the total number of shares to the total number of plays is used as the sharing rate. The play rate, like rate, collection rate, and sharing rate are the four dynamic quality evaluation indicators for measuring dynamic quality.
这四个动态质量评价指标的重要程度是不同的，重要程度由高到低依次为分享率、点赞率、收藏率、游玩率，因此，按照重要程度为每个指标设置对应的权重，将四个动态质量评价指标的权重和作为动态质量评分。The importance of these four dynamic quality evaluation indicators is different. The importance, from high to low, is sharing rate, like rate, collection rate, and play rate. Therefore, a corresponding weight is set for each indicator according to its importance, and the sum of the weights of the four dynamic quality evaluation indicators is used as the dynamic quality score.
示例性的，动态质量评分＝1×游玩率+18×点赞率+10×收藏率+20×分享率。Exemplarily, dynamic quality score = 1×play rate + 18×like rate + 10×collection rate + 20×sharing rate.
步骤S203，根据近期原创内容的已发布时长，确定时间衰减系数。Step S203, determining a time decay coefficient according to the length of time that the recent original content has been published.
时间衰减是指一个近期原创内容在发布之后，其喜爱度评分会随着时间流逝而逐渐降低，直至为零。为了防止经典近期原创内容长期霸榜高级内容池，造成用户审美疲劳的问题。时间衰减系数可采用如下公式计算：时间衰减系数＝max(1-近期原创内容发布天数×0.02,0)，其中，0.02为公式中的系数，本领域技术人员可以根据实际情况选择该系数的具体取值。Time decay means that after a recent original content is released, its popularity score will gradually decrease over time until it reaches zero. In order to prevent classic recent original content from dominating the premium content pool for a long time and causing aesthetic fatigue among users. The time decay coefficient can be calculated using the following formula: time decay coefficient = max(1-number of days for recent original content release × 0.02, 0), where 0.02 is the coefficient in the formula, and technicians in this field can select the specific value of the coefficient according to actual conditions.
步骤S204，根据静态质量评分、动态质量评分及时间衰减系数，确定近期原创内容的喜爱度评分。Step S204, determining the popularity score of the recent original content according to the static quality score, the dynamic quality score and the time decay coefficient.
这里，可根据静态质量评分、动态质量评分及时间衰减系数，确定近期原创内容的喜爱度评分，例如，将静态质量评分、动态质量评分及时间衰减系数三者的乘积作为近期原创内容的喜爱度评分，也可以将动态质量评分与时间衰减系数的乘积作为近期原创内容的喜爱度评分，还可以先计算静态质量评分与动态质量评分之和，然后将两者之和与时间衰减系数的乘积作为近期原创内容的喜爱度评分。Here, the popularity score of recent original content can be determined based on the static quality score, dynamic quality score and time decay coefficient. For example, the product of the static quality score, the dynamic quality score and the time decay coefficient can be used as the popularity score of recent original content. The product of the dynamic quality score and the time decay coefficient can also be used as the popularity score of recent original content. The sum of the static quality score and the dynamic quality score can also be calculated first, and then the sum of the two and the product of the time decay coefficient can be used as the popularity score of recent original content.
在一可选实施例中，根据静态质量评分、动态质量评分及时间衰减系数，确定近期原创内容的喜爱度评分，包括：步骤e1至步骤e3。In an optional embodiment, the likeability score of recent original content is determined according to the static quality score, the dynamic quality score and the time decay coefficient, including: steps e1 to e3.
步骤e1，确定近期原创内容当前所在内容池的类型及级别。Step e1, determining the type and level of the content pool where the recent original content currently resides.
具体的，根据不同的使用场景特点采用不同的喜爱度评分计算公式。Specifically, different likeability score calculation formulas are used according to the characteristics of different usage scenarios.
在低级内容池中，由于近期原创内容对应的预设推荐比例低，会造成曝光次数较少，动态质量评分拥有较低的置信度，所以需要引入静态质量评分进行平衡，让创作者花费心血较多的近期原创内容有更大的概率进入高级内容池中。In the low-level content pool, since the preset recommendation ratio corresponding to recent original content is low, it will result in fewer exposures and the dynamic quality score has a lower confidence level. Therefore, it is necessary to introduce static quality scores to balance it so that recent original content that creators have spent more effort on has a greater probability of entering the high-level content pool.
在高级内容池中，由于近期原创内容对应的预设推荐比例高，会造成曝光次数较多，从而可以得到足量的用户反馈数据，动态质量评分就更加可信，这时无需考虑静态质量评分。In the premium content pool, since the preset recommendation ratio corresponding to recent original content is high, it will result in more exposure times, thereby obtaining sufficient user feedback data, and the dynamic quality score will be more credible. At this time, there is no need to consider the static quality score.
综上所述，将多个第一内容池划分为低级内容池及高级内容池，以上述示例为例，可将第一主动内容池D及第一被动内容池C划为低级内容池，将第一被动内容池B、第一被动内容池A及第一被动内容池S划分为高级内容池。To sum up, multiple first content pools are divided into low-level content pools and high-level content pools. Taking the above example, the first active content pool D and the first passive content pool C can be classified as low-level content pools, and the first passive content pool B, the first passive content pool A and the first passive content pool S can be classified as high-level content pools.
步骤e2，若当前所在内容池为第一主动内容池或者为低级别第一被动内容池，则选取第一喜爱度计算公式，将近期原创内容的静态质量评分、动态质量评分及时间衰减系数代入第一喜爱度计算公式中，确定近期原创内容的喜爱度评分。Step e2: If the current content pool is the first active content pool or the low-level first passive content pool, select the first likeability calculation formula, substitute the static quality score, dynamic quality score and time attenuation coefficient of the recent original content into the first likeability calculation formula, and determine the likeability score of the recent original content.
针对第一主动内容池D及第一被动内容池C，将近期原创内容的静态质量评分、动态质量评分及时间衰减系数三者的乘积，作为该近期原创内容的喜爱度评分。For the first active content pool D and the first passive content pool C, the product of the static quality score, the dynamic quality score and the time decay coefficient of the recent original content is used as the likeability score of the recent original content.
步骤e3，若当前所在内容池为高级别第一被动内容池，则选取第二喜爱度计算公式，将近期原创内容的动态质量评分及时间衰减系数代入第二喜爱度计算公式中，确定近期原创内容的喜爱度评分。Step e3, if the current content pool is a high-level first passive content pool, select the second likeness calculation formula, substitute the dynamic quality score and time decay coefficient of the recent original content into the second likeness calculation formula, and determine the likeness score of the recent original content.
针对第一被动内容池B、第一被动内容池A及第一被动内容池S，将近期原创内容的动态质量评分及时间衰减系数的乘积，作为该近期原创内容的喜爱度评分。For the first passive content pool B, the first passive content pool A and the first passive content pool S, the product of the dynamic quality score of the recent original content and the time decay coefficient is used as the likeability score of the recent original content.
在一可选实施例中，多个第一被动内容池中越高级别的第一被动内容池的内容池数量阈值越低。In an optional embodiment, a first passive content pool with a higher level among the plurality of first passive content pools has a lower content pool quantity threshold.
在一示例中，第一主动内容池D的内容池数量阈值为1600，第一被动内容池C的内容池数量阈值为800，第一被动内容池B的内容池数量阈值为400，第一被动内容池A的内容池数量阈值为200，第一被动内容池S的内容池数量阈值为100。随着内容池级别的升高，内容池数量阈值逐渐减少，多个第一内容池之间随着级别升高呈现金字塔形状。In one example, the content pool quantity threshold of the first active content pool D is 1600, the content pool quantity threshold of the first passive content pool C is 800, the content pool quantity threshold of the first passive content pool B is 400, the content pool quantity threshold of the first passive content pool A is 200, and the content pool quantity threshold of the first passive content pool S is 100. As the level of the content pool increases, the content pool quantity threshold gradually decreases, and the multiple first content pools present a pyramid shape as the level increases.
在一可选实施例中，方法还包括：按照第二预设推荐比例分别从每个第二内容池中选取处于不同用户喜爱度区间的历史热门内容推荐给用户；根据用户针对目标历史热门内容的反馈信息确定劣质历史热门内容，将劣质历史热门内容从多个第二内容池中移除；获取新的历史热门内容补充至多个第二内容池中的至少一个目标第二内容池中，按照推荐规则将新的历史热门内容推荐给用户。In an optional embodiment, the method also includes: selecting historical popular content in different user preference ranges from each second content pool according to a second preset recommendation ratio and recommending them to users; determining low-quality historical popular content based on user feedback information on target historical popular content, and removing the low-quality historical popular content from multiple second content pools; acquiring new historical popular content to supplement at least one target second content pool among the multiple second content pools, and recommending the new historical popular content to users according to the recommendation rules.
经过统计，用户通过推荐系统进行原创内容游玩的比例占40％，这就意味着还有60％的地图是通过游戏中的其他系统进行传播的，包括但不限于搜索系统、分享系统、房间系统和任务系统。According to statistics, 40% of users play original content through the recommendation system, which means that another 60% of the maps are spread through other systems in the game, including but not limited to the search system, sharing system, room system and task system.
为了进一步利用全局所有系统的反馈数据，本公开还利用排行榜系统进行全局反馈收集，每一场游玩后的反馈信息都会被记录下来，形成全服统一的喜爱度排行榜。与金字塔内容池推荐方法不同的是，排行榜的反馈数据不限于推荐系统局部之内，而是遍及到全服所有的反馈数据。In order to further utilize the feedback data of all global systems, the present disclosure also uses the ranking system to collect global feedback. The feedback information after each game will be recorded to form a unified popularity ranking list for the entire server. Unlike the pyramid content pool recommendation method, the feedback data of the ranking list is not limited to the local recommendation system, but covers all feedback data of the entire server.
在采用近期原创内容递进图池的同时，本公开还额外创建了一个历史热门内容的递进图池，即第二内容池，该第二内容池与第一内容池的主要区别是：将全服喜爱度排行榜作为扫描数据的源头，即将全服喜爱度排行榜中的历史热门内容放入第二资源库中，将第二资源库中的历史热门内容放入第二内容池中的第二主动内容池中，按照与第一内容池同样的递进方法进行第二内容池中历史热门内容的更新，以将劣质历史热门内容从第二内容池中移除。While adopting a progressive graph pool of recent original content, the present disclosure also creates an additional progressive graph pool of historical popular content, namely, a second content pool. The main difference between the second content pool and the first content pool is that the server-wide popularity ranking list is used as the source of scan data, that is, the historical popular content in the server-wide popularity ranking list is placed in the second resource library, and the historical popular content in the second resource library is placed in the second active content pool in the second content pool. The historical popular content in the second content pool is updated according to the same progressive method as the first content pool to remove low-quality historical popular content from the second content pool.
需要说明的是，在从第二资源库中获取历史热门内容时，选取过程加了随机性。首先，从第二资源库中选取排名靠前的预设数量的历史热门内容作为候选历史热门内容集合，然后，候选历史热门内容集合中的历史热门内容进行随机打乱，对随机打乱后的候选历史热门内容集合从头到尾依次选取所需数量的历史热门内容加入到多个第二内容池中。在每天设定时间点，对候选历史热门内容集合进行重置，重新回到头部位置，重新选取历史热门内容加入到多个第二内容池中，并且同一天中同一历史热门内容只有一次会被扫描进入第二内容池中。It should be noted that when obtaining historical popular content from the second resource library, randomness is added to the selection process. First, a preset number of historical popular content with high rankings is selected from the second resource library as a candidate historical popular content set, and then the historical popular content in the candidate historical popular content set is randomly shuffled, and the required number of historical popular content is selected from the randomly shuffled candidate historical popular content set from the beginning to the end to be added to multiple second content pools. At a set time point every day, the candidate historical popular content set is reset, returned to the head position, and historical popular content is reselected to be added to multiple second content pools, and the same historical popular content will only be scanned into the second content pool once on the same day.
相对于第一内容池而言，第二内容池中的初始原创内容的质量更高，可以弥补近期原创内容在初始曝光时，带来的低质量推荐的影响。同时，还可以方便用户回顾经典原创内容，让真正符合全服用户喜爱的原创内容可以长期出现在用户的推荐列表中。Compared with the first content pool, the initial original content in the second content pool is of higher quality, which can make up for the impact of low-quality recommendations brought by recent original content when it was initially exposed. At the same time, it can also facilitate users to review classic original content, so that original content that truly meets the preferences of all service users can appear in the user's recommendation list for a long time.
由于多个第二内容池中历史热门内容的更新过程与多个第一内容池中近期原创内容的更新过程相同，这里不再赘述。需要说明的是，多个第一内容池与多个第二内容池可同时存在，当某位用户打开推荐页面时，会从多个第一内容池中选择第一预设数量的近期原创内容，从多个第二内容池中选择第二预设数量的历史热门内容，然后将第一预设数量的近期原创内容与第二预设数量的历史热门内容混合在一起推荐给用户。Since the updating process of historical popular content in multiple second content pools is the same as the updating process of recent original content in multiple first content pools, it will not be described here. It should be noted that multiple first content pools and multiple second content pools can exist at the same time. When a user opens the recommendation page, a first preset number of recent original content will be selected from multiple first content pools, and a second preset number of historical popular content will be selected from multiple second content pools. Then, the first preset number of recent original content and the second preset number of historical popular content will be mixed together and recommended to the user.
与现有技术中游戏中的原创内容推荐方法相比，本公开能够直接获取新的近期原创内容，将新的近期原创内容推荐给用户，使得刚注册的用户能够获取到新的近期原创内容，也可以使新的近期发布内容得以展示，避免了出现冷启动，同时，能够从多个内容池中选取处于不同用户喜爱度区间的近期原创内容推荐给用户，增加了推荐的广度，解决了现有推荐方法存在冷启动、过度推荐和狭窄推荐的问题。Compared with the original content recommendation method in the game in the prior art, the present invention can directly obtain new recent original content and recommend the new recent original content to users, so that newly registered users can obtain new recent original content, and new recently released content can be displayed, avoiding cold start. At the same time, it can select recent original content in different user preference ranges from multiple content pools and recommend them to users, increasing the breadth of recommendation and solving the problems of cold start, over-recommendation and narrow recommendation in the existing recommendation methods.
基于同一发明构思，本公开实施例中还提供了与游戏中的原创内容推荐方法对应的游戏中的原创内容推荐装置，由于本公开实施例中的装置解决问题的原理与本公开实施例上述游戏中的原创内容推荐方法相似，因此装置的实施可以参见方法的实施，重复之处不再赘述。Based on the same inventive concept, the embodiments of the present disclosure also provide an original content recommendation device in a game corresponding to the original content recommendation method in a game. Since the principle of solving the problem by the device in the embodiments of the present disclosure is similar to the original content recommendation method in the game mentioned above in the embodiments of the present disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.
请参阅图4，图4为本公开实施例所提供的一种游戏中的原创内容推荐装置的结构示意图。如图4中所示，所述游戏中的原创内容推荐装置300包括：Please refer to FIG4 , which is a schematic diagram of the structure of an original content recommendation device in a game provided by an embodiment of the present disclosure. As shown in FIG4 , the original content recommendation device 300 in a game includes:
第一推荐模块301，用于按照第一预设推荐比例分别从不同等级的每个第一内容池中选取近期原创内容推荐给用户；The first recommendation module 301 is used to select recent original content from each first content pool of different levels and recommend it to the user according to a first preset recommendation ratio;
内容更新模块302，用于根据用户针对目标近期原创内容的反馈信息确定近期劣质内容，将所述近期劣质内容从多个第一内容池中移除；A content updating module 302, configured to determine recent low-quality content according to user feedback information on target recent original content, and remove the recent low-quality content from the plurality of first content pools;
第二推荐模块303，用于获取新的近期原创内容补充至所述多个第一内容池中的至少一个目标第一内容池，按照推荐规则将所述新的近期原创内容推荐给用户，所述新的近期原创内容为未放入过所述多个第一内容池中的近期原创内容。The second recommendation module 303 is used to obtain new recent original content to supplement at least one target first content pool among the multiple first content pools, and recommend the new recent original content to the user according to the recommendation rules. The new recent original content is recent original content that has not been placed in the multiple first content pools.
在一个可行的实施方案中，方法还包括：按照第二预设推荐比例分别从不同等级的每个第二内容池中选取历史热门内容推荐给用户；根据用户针对目标历史热门内容的反馈信息确定劣质历史热门内容，将劣质历史热门内容从多个第二内容池中移除；获取新的历史热门内容补充至多个第二内容池中的至少一个目标第二内容池中，按照推荐规则将新的历史热门内容推荐给用户。In a feasible implementation scheme, the method also includes: selecting historical popular content from each second content pool of different levels and recommending it to users according to a second preset recommendation ratio; determining low-quality historical popular content based on user feedback information on target historical popular content, and removing the low-quality historical popular content from multiple second content pools; acquiring new historical popular content to supplement at least one target second content pool among the multiple second content pools, and recommending the new historical popular content to users according to the recommendation rules.
在一个可行的实施方案中，至少一个第一目标内容池包括第一主动内容池，获取新的近期原创内容补充至多个第一内容池中的至少一个目标第一内容池，包括：当第一主动内容池中的近期原创内容的数量小于第一主动内容池对应的内容池数量阈值时，获取新的近期原创内容加入到第一主动内容池中，以使第一主动内容池中的近期原创内容的数量等于第一主动内容池对应的内容池数量阈值。In a feasible implementation scheme, at least one first target content pool includes a first active content pool, and acquiring new recent original content to supplement at least one target first content pool among multiple first content pools includes: when the amount of recent original content in the first active content pool is less than a content pool quantity threshold corresponding to the first active content pool, acquiring new recent original content and adding it to the first active content pool, so that the amount of recent original content in the first active content pool is equal to the content pool quantity threshold corresponding to the first active content pool.
在一个可行的实施方案中，在获取新的近期原创内容加入到第一主动内容池中之后，包括：为新的近期原创内容设置基础流量，并将新的近期原创内容设置为曝光状态；当新的近期原创内容被用户浏览时，将处于曝光状态的新的近期原创内容的基础流量减一；当新的近期原创内容的基础流量降低至0时，将新的近期原创内容的状态由曝光状态改为等待状态，处于等待状态的近期原创内容不会被推荐给用户；计算新的近期原创内容进入等待状态的累计时长，当累计时长达到等待时长阈值时，将新的近期原创内容由等待状态改为排名状态。In a feasible implementation scheme, after acquiring new recent original content and adding it to the first active content pool, the following steps are included: setting a basic flow rate for the new recent original content, and setting the new recent original content to an exposure state; when the new recent original content is browsed by a user, reducing the basic flow rate of the new recent original content in the exposure state by one; when the basic flow rate of the new recent original content is reduced to 0, changing the state of the new recent original content from an exposure state to a waiting state, and the recent original content in the waiting state will not be recommended to the user; calculating the cumulative time that the new recent original content enters the waiting state, and when the cumulative time reaches the waiting time threshold, changing the new recent original content from the waiting state to the ranking state.
在一个可行的实施方案中，多个第一内容池包括多个第一被动内容池，根据用户针对目标近期原创内容的反馈信息确定近期劣质内容，将近期劣质内容从多个第一内容池中移除，包括：确定第一主动内容池中处于排名状态的近期原创内容的数量；当处于排名状态的近期原创内容的数量达到第一评分数量阈值时，确定处于排名状态的近期原创内容的喜爱度评分；将第一主动内容池中喜爱度评分排名靠前的第一设定数量的近期原创内容作为近期优质内容，将近期优质内容转移到多个第一被动内容池中；利用近期优质内容，将近期劣质内容从多个第一内容池中移除。In a feasible implementation scheme, the multiple first content pools include multiple first passive content pools, and recent low-quality content is determined based on user feedback information on target recent original content, and the recent low-quality content is removed from the multiple first content pools, including: determining the number of recent original content in a ranked state in the first active content pool; when the number of recent original content in a ranked state reaches a first score quantity threshold, determining the popularity score of the recent original content in a ranked state; taking a first set number of recent original content with high popularity scores in the first active content pool as recent high-quality content, and transferring the recent high-quality content to the multiple first passive content pools; and removing the recent low-quality content from the multiple first content pools by using the recent high-quality content.
在一个可行的实施方案中，多个第一被动内容池按照各自存储的近期原创内容的推荐程度由高到低具备对应的级别，利用近期优质内容，将近期劣质内容从多个第一内容池中移除，包括：将第一设定数量的近期优质内容加入到最低级别的第一被动内容池中；由加入第一设定数量的近期优质内容的最低级别的第一被动内容池开始，递进地将每个第一被动内容池中的近期优质内容升级至高一级别的第一被动内容池，直至将次高级别的第一被动内容池中的近期优质内容升级至最高级别的第一被动内容池；由加入近期优质内容的最高级别的第一被动内容池开始，递进地将每个第一被动内容池中的近期劣质内容降级至低一级别的第一被动内容池，直至将第一设定数量的近期劣质内容从最低级别的第一被动内容池中移除。In a feasible implementation scheme, multiple first passive content pools have corresponding levels from high to low according to the recommendation degree of recent original content stored in each of them, and recent low-quality content is removed from the multiple first content pools by using recent high-quality content, including: adding a first set number of recent high-quality content to the first passive content pool of the lowest level; starting from the first passive content pool of the lowest level to which the first set number of recent high-quality content is added, progressively upgrading the recent high-quality content in each first passive content pool to a first passive content pool of a higher level, until the recent high-quality content in the first passive content pool of the second highest level is upgraded to the first passive content pool of the highest level; starting from the first passive content pool of the highest level to which the recent high-quality content is added, progressively downgrading the recent low-quality content in each first passive content pool to a first passive content pool of a lower level, until the first set number of recent low-quality content is removed from the first passive content pool of the lowest level.
在一个可行的实施方案中，由加入第一设定数量的近期优质内容的最低级别的第一被动内容池开始，递进地将每个第一被动内容池中的近期优质内容升级至高一级别的第一被动内容池，包括：选取最低级别的第一被动内容池作为第一升级被动内容池；确定第一升级被动内容池是否为最高级别的第一被动内容池；若第一升级被动内容池不是最高级别的第一被动内容池，从第一升级被动内容池中的近期原创内容中选取近期优质内容，将近期优质内容转移至比第一升级被动内容池的高一级别的第一被动内容池中；将高一级别的第一被动内容池作为新的第一升级被动内容池，返回执行从第一升级被动内容池中的近期原创内容中选取近期优质内容的步骤。In a feasible implementation scheme, starting from the lowest-level first passive content pool to which a first set number of recent high-quality contents are added, the recent high-quality contents in each first passive content pool are progressively upgraded to a first passive content pool of a higher level, including: selecting the lowest-level first passive content pool as the first upgraded passive content pool; determining whether the first upgraded passive content pool is the highest-level first passive content pool; if the first upgraded passive content pool is not the highest-level first passive content pool, selecting recent high-quality contents from the recent original contents in the first upgraded passive content pool, and transferring the recent high-quality contents to a first passive content pool of a higher level than the first upgraded passive content pool; using the higher-level first passive content pool as the new first upgraded passive content pool, and returning to execute the step of selecting recent high-quality contents from the recent original contents in the first upgraded passive content pool.
在一个可行的实施方案中，由加入近期优质内容的最高级别的第一被动内容池开始，递进地将每个第一被动内容池中的近期劣质内容降级至低一级别的第一被动内容池，包括：选取最高级别的第一被动内容池作为第一降级被动内容池；确定第一降级被动内容池是否为最低级别的第一被动内容池；若第一降级被动内容池不是最低级别的第一被动内容池，从第一降级被动内容池中的近期原创内容中选取近期劣质内容，将近期劣质内容转移至第一降级被动内容池的低一级别的第一被动内容池中；将低一级别的第一被动内容池作为新的第一降级被动内容池，返回执行从第一降级被动内容池中的近期原创内容中选取近期劣质内容的步骤。In a feasible implementation scheme, starting from the highest-level first passive content pool to which recent high-quality content is added, the recent low-quality content in each first passive content pool is progressively downgraded to a first passive content pool of a lower level, including: selecting the highest-level first passive content pool as the first downgraded passive content pool; determining whether the first downgraded passive content pool is the lowest-level first passive content pool; if the first downgraded passive content pool is not the lowest-level first passive content pool, selecting recent low-quality content from the recent original content in the first downgraded passive content pool, and transferring the recent low-quality content to the first passive content pool of a lower level of the first downgraded passive content pool; using the first passive content pool of a lower level as the new first downgraded passive content pool, and returning to execute the step of selecting recent low-quality content from the recent original content in the first downgraded passive content pool.
在一个可行的实施方案中，从第一升级被动内容池中的近期原创内容中选取近期优质内容，包括：在满足评比条件时，按照近期原创内容进入第一升级被动内容池的时间先后顺序进行排序，选取排名靠前的固定比例的近期原创内容作为近期候选优质内容；确定近期候选优质内容的当前喜爱度评分；按照当前喜爱度评分由高到低的顺序对近期候选优质内容进行排序，选取排名靠前的第一设定数量中与第一升级被动内容池对应的预设升级数量的近期候选优质内容作为最终的近期优质内容。In a feasible implementation scheme, recent high-quality content is selected from recent original content in the first upgraded passive content pool, including: when the evaluation conditions are met, the recent original content is sorted in the order of the time when it enters the first upgraded passive content pool, and a fixed proportion of the top-ranked recent original content is selected as the recent candidate high-quality content; the current popularity score of the recent candidate high-quality content is determined; the recent candidate high-quality content is sorted in descending order according to the current popularity score, and a preset upgraded number of recent candidate high-quality content corresponding to the first upgraded passive content pool is selected as the final recent high-quality content.
在一个可行的实施方案中，从第一降级被动内容池中的近期原创内容中选取近期劣质内容，包括：在满足评比条件时，按照近期原创内容进入第一降级被动内容池的时间先后顺序进行排序，选取排名靠前的固定比例的近期原创内容作为近期候选劣质内容；确定近期候选劣质内容的当前喜爱度评分；按照当前喜爱度评分由高到低的顺序对近期候选劣质内容进行排序，选取排名靠后的第一设定数量中与第一降级被动内容池对应的预设降级数量的近期候选劣质内容作为最终的近期劣质内容。In a feasible implementation scheme, recent low-quality content is selected from recent original content in the first degraded passive content pool, including: when the evaluation conditions are met, the recent original content is sorted in the chronological order of entering the first degraded passive content pool, and a fixed proportion of the top-ranked recent original content is selected as the recent candidate low-quality content; the current popularity score of the recent candidate low-quality content is determined; the recent candidate low-quality content is sorted in descending order according to the current popularity score, and a preset number of recent candidate low-quality content corresponding to the first degraded passive content pool is selected as the final recent low-quality content.
在一个可行的实施方案中，多个第一被动内容池对应的预设升级数量的数值大小随着第一被动内容池级别的增加逐渐减少。In a feasible implementation manner, the numerical values of the preset upgrade quantities corresponding to the plurality of first passive content pools gradually decrease as the level of the first passive content pool increases.
在一个可行的实施方案中，多个第一被动内容池对应的预设降级数量的数值大小随着第一被动内容池级别的降低逐渐增加。In a feasible implementation manner, the numerical values of the preset downgrade numbers corresponding to the plurality of first passive content pools gradually increase as the level of the first passive content pool decreases.
在一个可行的实施方案中，通过以下处理确定近期原创内容的喜爱度评分：根据近期原创内容的静态数据，确定静态质量评分；根据用户对近期原创内容的反馈信息，确定动态质量评分；根据近期原创内容的已发布时长，确定时间衰减系数；根据静态质量评分、动态质量评分及时间衰减系数，确定近期原创内容的喜爱度评分。In a feasible implementation scheme, the popularity score of recent original content is determined by the following processing: a static quality score is determined based on the static data of the recent original content; a dynamic quality score is determined based on user feedback information on the recent original content; a time decay coefficient is determined based on the published length of time of the recent original content; and a popularity score of the recent original content is determined based on the static quality score, the dynamic quality score and the time decay coefficient.
在一个可行的实施方案中，根据静态质量评分、动态质量评分及时间衰减系数，确定近期原创内容的喜爱度评分，包括：确定近期原创内容当前所在内容池的类型及级别；若当前所在内容池为第一主动内容池或者为低级别第一被动内容池，则选取第一喜爱度计算公式，将近期原创内容的静态质量评分、动态质量评分及时间衰减系数代入第一喜爱度计算公式中，确定近期原创内容的喜爱度评分；若当前所在内容池为高级别第一被动内容池，则选取第二喜爱度计算公式，将近期原创内容的动态质量评分及时间衰减系数代入第二喜爱度计算公式中，确定近期原创内容的喜爱度评分。In a feasible implementation scheme, the popularity score of recent original content is determined based on the static quality score, the dynamic quality score and the time decay coefficient, including: determining the type and level of the content pool in which the recent original content is currently located; if the current content pool is the first active content pool or the low-level first passive content pool, then selecting the first popularity calculation formula, substituting the static quality score, the dynamic quality score and the time decay coefficient of the recent original content into the first popularity calculation formula, and determining the popularity score of the recent original content; if the current content pool is the high-level first passive content pool, then selecting the second popularity calculation formula, substituting the dynamic quality score and the time decay coefficient of the recent original content into the second popularity calculation formula, and determining the popularity score of the recent original content.
在一个可行的实施方案中，多个第一被动内容池中越高级别的第一被动内容池的内容池数量阈值越低。In a feasible implementation manner, a first passive content pool with a higher level among the plurality of first passive content pools has a lower content pool quantity threshold.
请参阅图5，图5为本公开实施例所提供的一种电子设备的结构示意图。如图5中所示，所述电子设备400包括处理器410、存储器420和总线430。Please refer to FIG5 , which is a schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure. As shown in FIG5 , the electronic device 400 includes a processor 410 , a memory 420 and a bus 430 .
所述存储器420存储有所述处理器410可执行的机器可读指令，当电子设备运行如实施例中的一种游戏中的原创内容推荐方法时，所述处理器410与所述存储器420之间通过总线430通信，所述处理器410执行所述机器可读指令，所述处理器410方法项的前序部分，以执行以下步骤：The memory 420 stores machine-readable instructions executable by the processor 410. When the electronic device runs a method for recommending original content in a game as in the embodiment, the processor 410 communicates with the memory 420 via a bus 430, and the processor 410 executes the machine-readable instructions, the preamble of the method item of the processor 410, to perform the following steps:
按照第一预设推荐比例分别从不同等级的每个第一内容池中选取近期原创内容推荐给用户；Selecting recent original content from each first content pool of different levels according to a first preset recommendation ratio and recommending it to the user;
根据用户针对目标近期原创内容的反馈信息确定近期劣质内容，将近期劣质内容从多个第一内容池中移除；Determine recent low-quality content based on user feedback information on target recent original content, and remove the recent low-quality content from the multiple first content pools;
获取新的近期原创内容补充至多个第一内容池中的至少一个目标第一内容池，按照推荐规则将新的近期原创内容推荐给用户，新的近期原创内容为未放入过多个第一内容池中的近期原创内容。New recent original content is obtained and added to at least one target first content pool among the multiple first content pools, and the new recent original content is recommended to the user according to the recommendation rule. The new recent original content is recent original content that has not been placed in the multiple first content pools.
在一个可行的实施方案中，所述处理器410还用于：按照第二预设推荐比例分别从不同等级的每个第二内容池中选取历史热门内容推荐给用户；根据用户针对目标历史热门内容的反馈信息确定劣质历史热门内容，将劣质历史热门内容从多个第二内容池中移除；获取新的历史热门内容补充至多个第二内容池中的至少一个目标第二内容池中，按照推荐规则将新的历史热门内容推荐给用户。In a feasible implementation scheme, the processor 410 is also used to: select historical popular content from each second content pool of different levels and recommend it to users according to a second preset recommendation ratio; determine low-quality historical popular content based on user feedback information on target historical popular content, and remove the low-quality historical popular content from multiple second content pools; obtain new historical popular content and add it to at least one target second content pool among the multiple second content pools, and recommend the new historical popular content to users according to the recommendation rules.
在一个可行的实施方案中，至少一个第一目标内容池包括第一主动内容池，所述处理器410在执行获取新的近期原创内容补充至多个第一内容池中的至少一个目标第一内容池时，具体用于：当第一主动内容池中的近期原创内容的数量小于第一主动内容池对应的内容池数量阈值时，获取新的近期原创内容加入到第一主动内容池中，以使第一主动内容池中的近期原创内容的数量等于第一主动内容池对应的内容池数量阈值。In a feasible implementation scheme, at least one first target content pool includes a first active content pool. When executing the process of acquiring new recent original content to supplement at least one target first content pool among multiple first content pools, the processor 410 is specifically used to: when the number of recent original content in the first active content pool is less than the content pool quantity threshold corresponding to the first active content pool, acquire new recent original content and add it to the first active content pool, so that the number of recent original content in the first active content pool is equal to the content pool quantity threshold corresponding to the first active content pool.
在一个可行的实施方案中，所述处理器410在执行获取新的近期原创内容加入到第一主动内容池中之后，具体用于：为新的近期原创内容设置基础流量，并将新的近期原创内容设置为曝光状态；当新的近期原创内容被用户浏览时，将处于曝光状态的新的近期原创内容的基础流量减一；当新的近期原创内容的基础流量降低至0时，将新的近期原创内容的状态由曝光状态改为等待状态，处于等待状态的近期原创内容不会被推荐给用户；计算新的近期原创内容进入等待状态的累计时长，当累计时长达到等待时长阈值时，将新的近期原创内容由等待状态改为排名状态。In a feasible implementation scheme, after executing the acquisition of new recent original content and adding it to the first active content pool, the processor 410 is specifically used to: set a basic flow for the new recent original content, and set the new recent original content to an exposure state; when the new recent original content is browsed by a user, reduce the basic flow of the new recent original content in the exposure state by one; when the basic flow of the new recent original content is reduced to 0, change the state of the new recent original content from the exposure state to the waiting state, and the recent original content in the waiting state will not be recommended to the user; calculate the cumulative time that the new recent original content enters the waiting state, and when the cumulative time reaches the waiting time threshold, change the new recent original content from the waiting state to the ranking state.
在一个可行的实施方案中，多个第一内容池包括多个第一被动内容池，所述处理器410在执行根据用户针对目标近期原创内容的反馈信息确定近期劣质内容，将近期劣质内容从多个第一内容池中移除时，具体用于：确定第一主动内容池中处于排名状态的近期原创内容的数量；当处于排名状态的近期原创内容的数量达到第一评分数量阈值时，确定处于排名状态的近期原创内容的喜爱度评分；将第一主动内容池中喜爱度评分排名靠前的第一设定数量的近期原创内容作为近期优质内容，将近期优质内容转移到多个第一被动内容池中；利用近期优质内容，将近期劣质内容从多个第一内容池中移除。In a feasible implementation scheme, the multiple first content pools include multiple first passive content pools. When the processor 410 determines the recent low-quality content based on the user's feedback information on the target recent original content and removes the recent low-quality content from the multiple first content pools, it is specifically used to: determine the number of recent original content in a ranked state in the first active content pool; when the number of recent original content in a ranked state reaches a first score quantity threshold, determine the likeability score of the recent original content in a ranked state; use a first set number of recent original content with high likeability rankings in the first active content pool as recent high-quality content, and transfer the recent high-quality content to the multiple first passive content pools; and use the recent high-quality content to remove the recent low-quality content from the multiple first content pools.
在一个可行的实施方案中，多个第一被动内容池按照各自存储的近期原创内容的推荐程度由高到低具备对应的级别，所述处理器410在执行利用近期优质内容，将近期劣质内容从多个第一内容池中移除时，具体用于：将第一设定数量的近期优质内容加入到最低级别的第一被动内容池中；由加入第一设定数量的近期优质内容的最低级别的第一被动内容池开始，递进地将每个第一被动内容池中的近期优质内容升级至高一级别的第一被动内容池，直至将次高级别的第一被动内容池中的近期优质内容升级至最高级别的第一被动内容池；由加入近期优质内容的最高级别的第一被动内容池开始，递进地将每个第一被动内容池中的近期劣质内容降级至低一级别的第一被动内容池，直至将第一设定数量的近期劣质内容从最低级别的第一被动内容池中移除。In a feasible implementation scheme, the plurality of first passive content pools have corresponding levels from high to low according to the recommendation degree of the recent original content stored in each of them. When the processor 410 executes the method of removing the recent low-quality content from the plurality of first content pools by utilizing the recent high-quality content, it is specifically used to: add a first set number of recent high-quality content to the first passive content pool of the lowest level; starting from the first passive content pool of the lowest level to which the first set number of recent high-quality content is added, progressively upgrade the recent high-quality content in each first passive content pool to a first passive content pool of a higher level, until the recent high-quality content in the first passive content pool of the second highest level is upgraded to the first passive content pool of the highest level; starting from the first passive content pool of the highest level to which the recent high-quality content is added, progressively downgrade the recent low-quality content in each first passive content pool to a first passive content pool of a lower level, until the first set number of recent low-quality content is removed from the first passive content pool of the lowest level.
在一个可行的实施方案中，所述处理器410在执行由加入第一设定数量的近期优质内容的最低级别的第一被动内容池开始，递进地将每个第一被动内容池中的近期优质内容升级至高一级别的第一被动内容池时，具体用于：选取最低级别的第一被动内容池作为第一升级被动内容池；确定第一升级被动内容池是否为最高级别的第一被动内容池；若第一升级被动内容池不是最高级别的第一被动内容池，从第一升级被动内容池中的近期原创内容中选取近期优质内容，将近期优质内容转移至比第一升级被动内容池的高一级别的第一被动内容池中；将高一级别的第一被动内容池作为新的第一升级被动内容池，返回执行从第一升级被动内容池中的近期原创内容中选取近期优质内容的步骤。In one feasible embodiment, the processor 410 performs the minimum level of adding a first set amount of recent premium content. Starting from the first passive content pool, when progressively upgrading the recent high-quality content in each first passive content pool to a first passive content pool of a higher level, the method is specifically used to: select the first passive content pool of the lowest level as the first upgraded passive content pool; determine whether the first upgraded passive content pool is the first passive content pool of the highest level; if the first upgraded passive content pool is not the first passive content pool of the highest level, select recent high-quality content from the recent original content in the first upgraded passive content pool, and transfer the recent high-quality content to the first passive content pool of a higher level than the first upgraded passive content pool; use the first passive content pool of the higher level as the new first upgraded passive content pool, and return to execute the step of selecting recent high-quality content from the recent original content in the first upgraded passive content pool.
在一个可行的实施方案中，所述处理器410在执行由加入近期优质内容的最高级别的第一被动内容池开始，递进地将每个第一被动内容池中的近期劣质内容降级至低一级别的第一被动内容池时，具体用于：选取最高级别的第一被动内容池作为第一降级被动内容池；确定第一降级被动内容池是否为最低级别的第一被动内容池；若第一降级被动内容池不是最低级别的第一被动内容池，从第一降级被动内容池中的近期原创内容中选取近期劣质内容，将近期劣质内容转移至第一降级被动内容池的低一级别的第一被动内容池中；将低一级别的第一被动内容池作为新的第一降级被动内容池，返回执行从第一降级被动内容池中的近期原创内容中选取近期劣质内容的步骤。In a feasible implementation scheme, when the processor 410 executes the process of progressively downgrading the recent low-quality content in each first passive content pool to a first passive content pool of a lower level starting from the first passive content pool of the highest level to which recent high-quality content is added, the processor 410 is specifically used to: select the first passive content pool of the highest level as the first downgraded passive content pool; determine whether the first downgraded passive content pool is the first passive content pool of the lowest level; if the first downgraded passive content pool is not the first passive content pool of the lowest level, select recent low-quality content from the recent original content in the first downgraded passive content pool, and transfer the recent low-quality content to the first passive content pool of a lower level of the first downgraded passive content pool; use the first passive content pool of a lower level as the new first downgraded passive content pool, and return to the step of selecting recent low-quality content from the recent original content in the first downgraded passive content pool.
在一个可行的实施方案中，所述处理器410在执行从第一升级被动内容池中的近期原创内容中选取近期优质内容时，具体用于：在满足评比条件时，按照近期原创内容进入第一升级被动内容池的时间先后顺序进行排序，选取排名靠前的固定比例的近期原创内容作为近期候选优质内容；确定近期候选优质内容的当前喜爱度评分；按照当前喜爱度评分由高到低的顺序对近期候选优质内容进行排序，选取排名靠前的第一设定数量中与第一升级被动内容池对应的预设升级数量的近期候选优质内容作为最终的近期优质内容。In a feasible implementation scheme, when the processor 410 is executing the selection of recent high-quality content from the recent original content in the first upgraded passive content pool, it is specifically used to: when the evaluation conditions are met, sort the recent original content in the chronological order of their entry into the first upgraded passive content pool, and select a fixed proportion of the top-ranked recent original content as the recent candidate high-quality content; determine the current popularity score of the recent candidate high-quality content; sort the recent candidate high-quality content in descending order according to the current popularity score, and select a preset upgraded number of recent candidate high-quality content corresponding to the first upgraded passive content pool from a first set number of top-ranked recent original content as the final recent high-quality content.
在一个可行的实施方案中，所述处理器410在执行从第一降级被动内容池中的近期原创内容中选取近期劣质内容时，具体用于：在满足评比条件时，按照近期原创内容进入第一降级被动内容池的时间先后顺序进行排序，选取排名靠前的固定比例的近期原创内容作为近期候选劣质内容；确定近期候选劣质内容的当前喜爱度评分；按照当前喜爱度评分由高到低的顺序对近期候选劣质内容进行排序，选取排名靠后的第一设定数量中与第一降级被动内容池对应的预设降级数量的近期候选劣质内容作为最终的近期劣质内容。In a feasible implementation scheme, when the processor 410 is executing the selection of recent low-quality content from the recent original content in the first degraded passive content pool, it is specifically used to: when the evaluation conditions are met, sort the recent original content in the chronological order of their entry into the first degraded passive content pool, and select a fixed proportion of the top-ranked recent original content as the recent candidate low-quality content; determine the current popularity score of the recent candidate low-quality content; sort the recent candidate low-quality content in descending order of the current popularity score, and select a preset downgraded number of recent candidate low-quality content corresponding to the first degraded passive content pool from the first set number of low-ranked recent candidate low-quality content as the final recent low-quality content.
在一个可行的实施方案中，多个第一被动内容池对应的预设升级数量的数值大小随着第一被动内容池级别的增加逐渐减少。In a feasible implementation manner, the numerical values of the preset upgrade quantities corresponding to the plurality of first passive content pools gradually decrease as the level of the first passive content pool increases.
在一个可行的实施方案中，多个第一被动内容池对应的预设降级数量的数值大小随着第一被动内容池级别的降低逐渐增加。In a feasible implementation manner, the numerical values of the preset downgrade numbers corresponding to the plurality of first passive content pools gradually increase as the level of the first passive content pool decreases.
在一个可行的实施方案中，所述处理器410通过以下处理确定近期原创内容的喜爱度评分：根据近期原创内容的静态数据，确定静态质量评分；根据用户对近期原创内容的反馈信息，确定动态质量评分；根据近期原创内容的已发布时长，确定时间衰减系数；根据静态质量评分、动态质量评分及时间衰减系数，确定近期原创内容的喜爱度评分。In a feasible implementation scheme, the processor 410 determines the likeability score of recent original content by the following processing: determining a static quality score based on static data of the recent original content; determining a dynamic quality score based on user feedback information on the recent original content; determining a time decay coefficient based on the published length of time of the recent original content; determining a likeability score of the recent original content based on the static quality score, the dynamic quality score and the time decay coefficient.
在一个可行的实施方案中，所述处理器410在执行根据静态质量评分、动态质量评分及时间衰减系数，确定近期原创内容的喜爱度评分时，具体用于：确定近期原创内容当前所在内容池的类型及级别；若当前所在内容池为第一主动内容池或者为低级别第一被动内容池，则选取第一喜爱度计算公式，将近期原创内容的静态质量评分、动态质量评分及时间衰减系数代入第一喜爱度计算公式中，确定近期原创内容的喜爱度评分；若当前所在内容池为高级别第一被动内容池，则选取第二喜爱度计算公式，将近期原创内容的动态质量评分及时间衰减系数代入第二喜爱度计算公式中，确定近期原创内容的喜爱度评分。In a feasible implementation scheme, when the processor 410 determines the likeness score of recent original content based on the static quality score, the dynamic quality score and the time decay coefficient, it is specifically used to: determine the type and level of the content pool in which the recent original content is currently located; if the current content pool is the first active content pool or the low-level first passive content pool, then select the first likeness calculation formula, substitute the static quality score, the dynamic quality score and the time decay coefficient of the recent original content into the first likeness calculation formula, and determine the likeness score of the recent original content; if the current content pool is the high-level first passive content pool, then select the second likeness calculation formula, substitute the dynamic quality score and the time decay coefficient of the recent original content into the second likeness calculation formula, and determine the likeness score of the recent original content.
在一个可行的实施方案中，多个第一被动内容池中越高级别的第一被动内容池的内容池数量阈值越低。In a feasible implementation manner, a first passive content pool with a higher level among the plurality of first passive content pools has a lower content pool quantity threshold.
通过上述方式，直接获取新的近期原创内容，将新的近期原创内容推荐给用户，使得刚注册的用户能够获取到新的近期原创内容，也可以使新的近期发布内容得以展示，避免了出现冷启动，同时，能够从多个内容池中选取处于不同用户喜爱度区间的近期原创内容推荐给用户，增加了推荐的广度，解决了现有推荐方法存在冷启动、过度推荐和狭窄推荐的问题。Through the above method, new recent original content can be directly obtained and recommended to users, so that newly registered users can obtain new recent original content, and new recently released content can be displayed, avoiding cold start. At the same time, recent original content in different user preference ranges can be selected from multiple content pools and recommended to users, increasing the breadth of recommendations and solving the problems of cold start, over-recommendation and narrow recommendation in existing recommendation methods.
本公开实施例还提供一种计算机可读存储介质，该计算机可读存储介质上存储有计算机程序，该计算机程序被处理器运行时执行，所述处理器执行以下步骤：The present disclosure also provides a computer-readable storage medium, on which a computer program is stored. The computer program is executed when a processor is running, and the processor performs the following steps:
按照第一预设推荐比例分别从不同等级的每个第一内容池中选取近期原创内容推荐给用户；Selecting recent original content from each first content pool of different levels according to a first preset recommendation ratio and recommending it to the user;
根据用户针对目标近期原创内容的反馈信息确定近期劣质内容，将近期劣质内容从多个第一内容池中移除；Determine recent low-quality content based on user feedback information on target recent original content, and remove the recent low-quality content from the multiple first content pools;
获取新的近期原创内容补充至多个第一内容池中的至少一个目标第一内容池，按照推荐规则将新的近期原创内容推荐给用户，新的近期原创内容为未放入过多个第一内容池中的近期原创内容。New recent original content is obtained and added to at least one target first content pool among the multiple first content pools, and the new recent original content is recommended to the user according to the recommendation rule. The new recent original content is recent original content that has not been placed in the multiple first content pools.
在一个可行的实施方案中，所述处理器还用于：按照第二预设推荐比例分别从不同等级的每个第二内容池中选取历史热门内容推荐给用户；根据用户针对目标历史热门内容的反馈信息确定劣质历史热门内容，将劣质历史热门内容从多个第二内容池中移除；获取新的历史热门内容补充至多个第二内容池中的至少一个目标第二内容池中，按照推荐规则将新的历史热门内容推荐给用户。In a feasible implementation scheme, the processor is also used to: select historical popular content from each second content pool of different levels and recommend it to users according to a second preset recommendation ratio; determine low-quality historical popular content based on user feedback information on target historical popular content, and remove the low-quality historical popular content from multiple second content pools; obtain new historical popular content and supplement it to at least one target second content pool among the multiple second content pools, and recommend the new historical popular content to users according to the recommendation rules.
在一个可行的实施方案中，至少一个第一目标内容池包括第一主动内容池，所述处理器在执行获取新的近期原创内容补充至多个第一内容池中的至少一个目标第一内容池时，具体用于：当第一主动内容池中的近期原创内容的数量小于第一主动内容池对应的内容池数量阈值时，获取新的近期原创内容加入到第一主动内容池中，以使第一主动内容池中的近期原创内容的数量等于第一主动内容池对应的内容池数量阈值。In a feasible implementation scheme, at least one first target content pool includes a first active content pool. When the processor executes the function of acquiring new recent original content to supplement at least one target first content pool among multiple first content pools, the processor is specifically used to: when the number of recent original content in the first active content pool is less than a content pool quantity threshold corresponding to the first active content pool, acquire new recent original content and add it to the first active content pool, so that the number of recent original content in the first active content pool is equal to the content pool quantity threshold corresponding to the first active content pool.
在一个可行的实施方案中，所述处理器在执行获取新的近期原创内容加入到第一主动内容池中之后，具体用于：为新的近期原创内容设置基础流量，并将新的近期原创内容设置为曝光状态；当新的近期原创内容被用户浏览时，将处于曝光状态的新的近期原创内容的基础流量减一；当新的近期原创内容的基础流量降低至0时，将新的近期原创内容的状态由曝光状态改为等待状态，处于等待状态的近期原创内容不会被推荐给用户；计算新的近期原创内容进入等待状态的累计时长，当累计时长达到等待时长阈值时，将新的近期原创内容由等待状态改为排名状态。In a feasible implementation scheme, after executing the acquisition of new recent original content and adding it to the first active content pool, the processor is specifically used to: set a basic flow for the new recent original content, and set the new recent original content to an exposure state; when the new recent original content is browsed by a user, reduce the basic flow of the new recent original content in the exposure state by one; when the basic flow of the new recent original content is reduced to 0, change the state of the new recent original content from the exposure state to the waiting state, and the recent original content in the waiting state will not be recommended to the user; calculate the cumulative time that the new recent original content enters the waiting state, and when the cumulative time reaches the waiting time threshold, change the new recent original content from the waiting state to the ranking state.
在一个可行的实施方案中，多个第一内容池包括多个第一被动内容池，所述处理器在执行根据用户针对目标近期原创内容的反馈信息确定近期劣质内容，将近期劣质内容从多个第一内容池中移除时，具体用于：确定第一主动内容池中处于排名状态的近期原创内容的数量；当处于排名状态的近期原创内容的数量达到第一评分数量阈值时，确定处于排名状态的近期原创内容的喜爱度评分；将第一主动内容池中喜爱度评分排名靠前的第一设定数量的近期原创内容作为近期优质内容，将近期优质内容转移到多个第一被动内容池中；利用近期优质内容，将近期劣质内容从多个第一内容池中移除。In a feasible implementation scheme, the multiple first content pools include multiple first passive content pools. When the processor determines the recent low-quality content based on the user's feedback information on the target recent original content and removes the recent low-quality content from the multiple first content pools, it is specifically used to: determine the number of recent original content in a ranked state in the first active content pool; when the number of recent original content in a ranked state reaches a first score quantity threshold, determine the likeability score of the recent original content in a ranked state; use a first set number of recent original content with high likeability scores in the first active content pool as recent high-quality content, and transfer the recent high-quality content to the multiple first passive content pools; and use the recent high-quality content to remove the recent low-quality content from the multiple first content pools.
在一个可行的实施方案中，多个第一被动内容池按照各自存储的近期原创内容的推荐程度由高到低具备对应的级别，所述处理器在执行利用近期优质内容，将近期劣质内容从多个第一内容池中移除时，具体用于：将第一设定数量的近期优质内容加入到最低级别的第一被动内容池中；由加入第一设定数量的近期优质内容的最低级别的第一被动内容池开始，递进地将每个第一被动内容池中的近期优质内容升级至高一级别的第一被动内容池，直至将次高级别的第一被动内容池中的近期优质内容升级至最高级别的第一被动内容池；由加入近期优质内容的最高级别的第一被动内容池开始，递进地将每个第一被动内容池中的近期劣质内容降级至低一级别的第一被动内容池，直至将第一设定数量的近期劣质内容从最低级别的第一被动内容池中移除。In a feasible implementation scheme, multiple first passive content pools have corresponding levels from high to low according to the recommendation degree of recent original content stored in each of them. When the processor executes the method of removing recent low-quality content from multiple first content pools by using recent high-quality content, it is specifically used to: add a first set number of recent high-quality content to the first passive content pool of the lowest level; starting from the first passive content pool of the lowest level to which the first set number of recent high-quality content is added, progressively upgrade the recent high-quality content in each first passive content pool to a first passive content pool of a higher level, until the recent high-quality content in the first passive content pool of the second highest level is upgraded to the first passive content pool of the highest level; starting from the first passive content pool of the highest level to which the recent high-quality content is added, progressively downgrade the recent low-quality content in each first passive content pool to a first passive content pool of a lower level, until the first set number of recent low-quality content is removed from the first passive content pool of the lowest level.
在一个可行的实施方案中，所述处理器在执行由加入第一设定数量的近期优质内容的最低级别的第一被动内容池开始，递进地将每个第一被动内容池中的近期优质内容升级至高一级别的第一被动内容池时，具体用于：选取最低级别的第一被动内容池作为第一升级被动内容池；确定第一升级被动内容池是否为最高级别的第一被动内容池；若第一升级被动内容池不是最高级别的第一被动内容池，从第一升级被动内容池中的近期原创内容中选取近期优质内容，将近期优质内容转移至比第一升级被动内容池的高一级别的第一被动内容池中；将高一级别的第一被动内容池作为新的第一升级被动内容池，返回执行从第一升级被动内容池中的近期原创内容中选取近期优质内容的步骤。In a feasible implementation scheme, when executing the step of progressively upgrading the recent high-quality content in each first passive content pool to a first passive content pool of a higher level starting from the first passive content pool of the lowest level to which a first set number of recent high-quality content is added, the processor is specifically used to: select the first passive content pool of the lowest level as the first upgraded passive content pool; determine whether the first upgraded passive content pool is the first passive content pool of the highest level; if the first upgraded passive content pool is not the first passive content pool of the highest level, select recent high-quality content from the recent original content in the first upgraded passive content pool, and transfer the recent high-quality content to a first passive content pool of a higher level than the first upgraded passive content pool; use the first passive content pool of the higher level as the new first upgraded passive content pool, and return to execute the step of selecting recent high-quality content from the recent original content in the first upgraded passive content pool.
在一个可行的实施方案中，所述处理器在执行由加入近期优质内容的最高级别的第一被动内容池开始，递进地将每个第一被动内容池中的近期劣质内容降级至低一级别的第一被动内容池时，具体用于：选取最高级别的第一被动内容池作为第一降级被动内容池；确定第一降级被动内容池是否为最低级别的第一被动内容池；若第一降级被动内容池不是最低级别的第一被动内容池，从第一降级被动内容池中的近期原创内容中选取近期劣质内容，将近期劣质内容转移至第一降级被动内容池的低一级别的第一被动内容池中；将低一级别的第一被动内容池作为新的第一降级被动内容池，返回执行从第一降级被动内容池中的近期原创内容中选取近期劣质内容的步骤。In a feasible implementation scheme, when the processor executes the step of progressively downgrading the recent low-quality content in each first passive content pool to a first passive content pool of a lower level starting from the first passive content pool of the highest level to which recent high-quality content is added, the processor is specifically used to: select the first passive content pool of the highest level as the first downgraded passive content pool; determine whether the first downgraded passive content pool is the first passive content pool of the lowest level; if the first downgraded passive content pool is not the first passive content pool of the lowest level, select recent low-quality content from the recent original content in the first downgraded passive content pool, and transfer the recent low-quality content to the first passive content pool of a lower level of the first downgraded passive content pool; use the first passive content pool of a lower level as the new first downgraded passive content pool, and return to execute the step of selecting recent low-quality content from the recent original content in the first downgraded passive content pool.
在一个可行的实施方案中，所述处理器在执行从第一升级被动内容池中的近期原创内容中选取近期优质内容时，具体用于：在满足评比条件时，按照近期原创内容进入第一升级被动内容池的时间先后顺序进行排序，选取排名靠前的固定比例的近期原创内容作为近期候选优质内容；确定近期候选优质内容的当前喜爱度评分；按照当前喜爱度评分由高到低的顺序对近期候选优质内容进行排序，选取排名靠前的第一设定数量中与第一升级被动内容池对应的预设升级数量的近期候选优质内容作为最终的近期优质内容。In a feasible implementation scheme, when the processor executes the selection of recent high-quality content from the recent original content in the first upgraded passive content pool, it is specifically used to: when the evaluation conditions are met, sort the recent original content in the chronological order of their entry into the first upgraded passive content pool, and select a fixed proportion of the top-ranked recent original content as the recent candidate high-quality content; determine the current popularity score of the recent candidate high-quality content; sort the recent candidate high-quality content in descending order according to the current popularity score, and select a preset upgraded number of recent candidate high-quality content corresponding to the first upgraded passive content pool from a first set number of top-ranked recent original content as the final recent high-quality content.
在一个可行的实施方案中，所述处理器在执行从第一降级被动内容池中的近期原创内容中选取近期劣质内容时，具体用于：在满足评比条件时，按照近期原创内容进入第一降级被动内容池的时间先后顺序进行排序，选取排名靠前的固定比例的近期原创内容作为近期候选劣质内容；确定近期候选劣质内容的当前喜爱度评分；按照当前喜爱度评分由高到低的顺序对近期候选劣质内容进行排序，选取排名靠后的第一设定数量中与第一降级被动内容池对应的预设降级数量的近期候选劣质内容作为最终的近期劣质内容。In a feasible implementation scheme, when the processor selects recent low-quality content from recent original content in the first downgraded passive content pool, the processor is specifically used to: sort the recent original content according to the time sequence of entering the first downgraded passive content pool when the evaluation condition is met, and select a fixed proportion of the recent original content with a high ranking as the recent candidate low-quality content; determine the recent candidate low-quality content current popularity score; sorting the recent candidate low-quality contents in descending order according to the current popularity score, and selecting the recent candidate low-quality contents of the preset downgraded number corresponding to the first downgraded passive content pool from the first set number of low-ranking ones as the final recent low-quality contents.
在一个可行的实施方案中，多个第一被动内容池对应的预设升级数量的数值大小随着第一被动内容池级别的增加逐渐减少。In a feasible implementation manner, the numerical values of the preset upgrade quantities corresponding to the plurality of first passive content pools gradually decrease as the level of the first passive content pool increases.
在一个可行的实施方案中，多个第一被动内容池对应的预设降级数量的数值大小随着第一被动内容池级别的降低逐渐增加。In a feasible implementation manner, the numerical values of the preset downgrade numbers corresponding to the plurality of first passive content pools gradually increase as the level of the first passive content pool decreases.
在一个可行的实施方案中，所述处理器通过以下处理确定近期原创内容的喜爱度评分：根据近期原创内容的静态数据，确定静态质量评分；根据用户对近期原创内容的反馈信息，确定动态质量评分；根据近期原创内容的已发布时长，确定时间衰减系数；根据静态质量评分、动态质量评分及时间衰减系数，确定近期原创内容的喜爱度评分。In a feasible implementation scheme, the processor determines the likeability score of recent original content by the following processing: determining a static quality score based on static data of the recent original content; determining a dynamic quality score based on user feedback information on the recent original content; determining a time decay coefficient based on the published length of time of the recent original content; determining a likeability score of the recent original content based on the static quality score, the dynamic quality score and the time decay coefficient.
在一个可行的实施方案中，所述处理器在执行根据静态质量评分、动态质量评分及时间衰减系数，确定近期原创内容的喜爱度评分时，具体用于：确定近期原创内容当前所在内容池的类型及级别；若当前所在内容池为第一主动内容池或者为低级别第一被动内容池，则选取第一喜爱度计算公式，将近期原创内容的静态质量评分、动态质量评分及时间衰减系数代入第一喜爱度计算公式中，确定近期原创内容的喜爱度评分；若当前所在内容池为高级别第一被动内容池，则选取第二喜爱度计算公式，将近期原创内容的动态质量评分及时间衰减系数代入第二喜爱度计算公式中，确定近期原创内容的喜爱度评分。In a feasible implementation scheme, when the processor determines the likeability score of recent original content based on the static quality score, the dynamic quality score and the time decay coefficient, it is specifically used to: determine the type and level of the content pool in which the recent original content is currently located; if the current content pool is the first active content pool or the low-level first passive content pool, select the first likeability calculation formula, substitute the static quality score, the dynamic quality score and the time decay coefficient of the recent original content into the first likeability calculation formula, and determine the likeability score of the recent original content; if the current content pool is the high-level first passive content pool, select the second likeability calculation formula, substitute the dynamic quality score and the time decay coefficient of the recent original content into the second likeability calculation formula, and determine the likeability score of the recent original content.
在一个可行的实施方案中，多个第一被动内容池中越高级别的第一被动内容池的内容池数量阈值越低。In a feasible implementation manner, a first passive content pool with a higher level among the plurality of first passive content pools has a lower content pool quantity threshold.
通过上述方式，直接获取新的近期原创内容，将新的近期原创内容推荐给用户，使得刚注册的用户能够获取到新的近期原创内容，也可以使新的近期发布内容得以展示，避免了出现冷启动，同时，能够从多个内容池中选取处于不同用户喜爱度区间的近期原创内容推荐给用户，增加了推荐的广度，解决了现有推荐方法存在冷启动、过度推荐和狭窄推荐的问题。Through the above method, new recent original content can be directly obtained and recommended to users, so that newly registered users can obtain new recent original content, and new recently released content can be displayed, avoiding cold start. At the same time, recent original content in different user preference ranges can be selected from multiple content pools and recommended to users, increasing the breadth of recommendations and solving the problems of cold start, over-recommendation and narrow recommendation in existing recommendation methods.
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统、装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.
在本公开所提供的几个实施例中，应该理解到，所揭露的系统、装置和方法，可以通过其它的方式实现。以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，又例如，多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些通信接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。In the several embodiments provided in the present disclosure, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
另外，在本公开各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
所述功能如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个处理器可执行的非易失的计算机可读取存储介质中。基于这样的理解，本公开的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本公开各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(Read-Only Memory，ROM)、随机存取存储器(Random Access Memory，RAM)、磁碟或者光盘等各种可以存储程序代码的介质。If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that can be executed by a processor. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.
最后应说明的是：以上所述实施例，仅为本公开的具体实施方式，用以说明本公开的技术方案，而非对其限制，本公开的保护范围并不局限于此，尽管参照前述实施例对本公开进行了详细的说明，本领域的普通技术人员应当理解：任何熟悉本技术领域的技术人员在本公开揭露的技术范围内，其依然可以对前述实施例所记载的技术方案进行修改或可轻易想到变化，或者对其中部分技术特征进行等同替换；而这些修改、变化或者替换，并不使相应技术方案的本质脱离本公开实施例技术方案的精神和范围，都应涵盖在本公开的保护范围之内。因此，本公开的保护范围应以权利要求的保护范围为准。Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present disclosure, which are used to illustrate the technical solutions of the present disclosure, rather than to limit them. The protection scope of the present disclosure is not limited thereto. Although the present disclosure is described in detail with reference to the aforementioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the aforementioned embodiments within the technical scope disclosed in the present disclosure, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be based on the protection scope of the claims.
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| CN202311284381.6ACN117216402A (en) | 2023-10-07 | 2023-10-07 | Original content recommendation method and device in game, electronic equipment and medium |
| Publication Number | Publication Date |
|---|---|
| WO2025073209A1true WO2025073209A1 (en) | 2025-04-10 |
| WO2025073209A9 WO2025073209A9 (en) | 2025-05-30 |
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/CN2024/114344PendingWO2025073209A1 (en) | 2023-10-07 | 2024-08-23 | In-game original content recommendation method and apparatus, electronic device, and medium |
| Country | Link |
|---|---|
| CN (1) | CN117216402A (en) |
| WO (1) | WO2025073209A1 (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117216402A (en)* | 2023-10-07 | 2023-12-12 | 网易（杭州）网络有限公司 | Original content recommendation method and device in game, electronic equipment and medium |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104239523A (en)* | 2014-09-17 | 2014-12-24 | 北京金山安全软件有限公司 | Recommendation method and device for application program and mobile terminal |
| US20150269488A1 (en)* | 2014-03-18 | 2015-09-24 | Outbrain Inc. | Provisioning personalized content recommendations |
| CN109450862A (en)* | 2018-10-17 | 2019-03-08 | Oppo广东移动通信有限公司 | Published information recommendation method and device, storage medium and terminal |
| CN112650931A (en)* | 2021-01-04 | 2021-04-13 | 杭州情咖网络技术有限公司 | Content recommendation method |
| CN116150503A (en)* | 2023-04-13 | 2023-05-23 | 北京创新乐知网络技术有限公司 | Recall processing method for content recommendation and content recommendation method |
| CN117216402A (en)* | 2023-10-07 | 2023-12-12 | 网易（杭州）网络有限公司 | Original content recommendation method and device in game, electronic equipment and medium |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111831891B (en)* | 2019-04-18 | 2024-08-13 | 第四范式（北京）技术有限公司 | Material recommendation method and system |
| CN115658932B (en)* | 2022-09-16 | 2025-08-12 | 福州畅昕信息科技有限公司 | Book cultivation promotion method, electronic equipment and storage medium |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150269488A1 (en)* | 2014-03-18 | 2015-09-24 | Outbrain Inc. | Provisioning personalized content recommendations |
| CN104239523A (en)* | 2014-09-17 | 2014-12-24 | 北京金山安全软件有限公司 | Recommendation method and device for application program and mobile terminal |
| CN109450862A (en)* | 2018-10-17 | 2019-03-08 | Oppo广东移动通信有限公司 | Published information recommendation method and device, storage medium and terminal |
| CN112650931A (en)* | 2021-01-04 | 2021-04-13 | 杭州情咖网络技术有限公司 | Content recommendation method |
| CN116150503A (en)* | 2023-04-13 | 2023-05-23 | 北京创新乐知网络技术有限公司 | Recall processing method for content recommendation and content recommendation method |
| CN117216402A (en)* | 2023-10-07 | 2023-12-12 | 网易（杭州）网络有限公司 | Original content recommendation method and device in game, electronic equipment and medium |
| Publication number | Publication date |
|---|---|
| WO2025073209A9 (en) | 2025-05-30 |
| CN117216402A (en) | 2023-12-12 |
| Publication | Publication Date | Title |
|---|---|---|
| CN108520076B (en) | Electronic book recommendation method, electronic device and computer storage medium | |
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| Coucheney et al. | Influence of search neutrality on the economics of advertisement-financed content |
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