技术领域Technical Field
本发明涉及电商技术领域，具体涉及一种基于人工智能的电商产品导购方法及装置。The present invention relates to the technical field of e-commerce, and in particular to an e-commerce product shopping guide method and device based on artificial intelligence.
背景技术Background technique
目前，整体来说，电子商务行业仍在稳步前进，伴随着互联网技术的不断提升，各大电子商务服务商都致力于向平台用户提供更专业化的服务，在最大程度上降低交易过程中所需要的成本，提高用户的购买欲。电子商务的发展模式对企业提出来许多新的要求，比如商品推荐的精准性、送货的时效性、商品的质量保证、退换货的便捷性等等更多新的要求。At present, overall, the e-commerce industry is still advancing steadily. With the continuous improvement of Internet technology, major e-commerce service providers are committed to providing more professional services to platform users, reducing the costs required in the transaction process to the greatest extent and increasing users' purchasing desire. The development model of e-commerce has put forward many new requirements for enterprises, such as the accuracy of product recommendations, the timeliness of delivery, the quality assurance of products, the convenience of returns and exchanges, and many more new requirements.
其中，最为突出的问题就是商品选购，其平台的商品推荐会影响到用户的商品选购。如何精准的向用户展示平台的个性化推荐，向用户进行合适的商品推荐是电商导购中不可缺少的一部分。Among them, the most prominent problem is product selection. The product recommendations of the platform will affect the user's product selection. How to accurately display the personalized recommendations of the platform to users and recommend appropriate products to users is an indispensable part of e-commerce shopping guide.
发明内容Summary of the invention
针对所述缺陷，本发明实施例公开了一种基于人工智能的电商产品导购方法及装置，其针对不同的用户提供个性化的精准导购推荐。In view of the above-mentioned defects, an embodiment of the present invention discloses an e-commerce product shopping guide method and device based on artificial intelligence, which provides personalized and accurate shopping guide recommendations for different users.
本发明实施例第一方面公开了一种基于人工智能的电商产品导购方法，包括：A first aspect of an embodiment of the present invention discloses an e-commerce product shopping guide method based on artificial intelligence, comprising:
响应于用户的搜索指令，获取所述搜索指令包含的搜索时间戳、关键字以及用户信息，所述用户信息包括账户信息和至少一个用户历史画像；所述用户历史画像包括面貌特征、身材信息、年龄信息、性别；In response to a user's search instruction, obtaining a search timestamp, a keyword, and user information contained in the search instruction, wherein the user information includes account information and at least one user history portrait; the user history portrait includes facial features, body shape information, age information, and gender;
基于账户信息获取用户的历史订单信息，并根据关键字从历史订单信息中筛选与关键字匹配的目标历史订单；Obtain the user's historical order information based on the account information, and filter the target historical orders matching the keywords from the historical order information according to the keywords;
分别计算目标历史订单中每一个历史订单的评分，选取评分在前N名的订单为参考订单；其中N为自然数，且N不等于0；Calculate the score of each historical order in the target historical orders respectively, and select the orders with the top N scores as reference orders; where N is a natural number and N is not equal to 0;
根据用户历史画像和关键字生成用户匹配的产品信息，基于所述产品信息和参考订单生成推荐产品，将推荐产品生成推荐购买页面发送给用户。Generate user-matching product information based on user historical portraits and keywords, generate recommended products based on the product information and reference orders, and generate a recommended purchase page for the recommended products and send it to the user.
作为一种可选的实施方式，在本发明实施例第一方面中，根据关键字从历史订单信息中筛选与关键字匹配的目标历史订单，包括：As an optional implementation manner, in the first aspect of the embodiment of the present invention, filtering the target historical orders matching the keyword from the historical order information according to the keyword includes:
删除历史订单信息中购物时间超过预设时间阈值的历史订单得到目标期限内的历史订单信息；Delete the historical orders whose purchase time exceeds the preset time threshold in the historical order information to obtain the historical order information within the target period;
获取关键字的产品类目，基于所述产品类目剔除目标期限内的历史订单信息中不匹配该产品类目的订单得到初选历史订单；Obtain the product category of the keyword, and based on the product category, remove orders that do not match the product category from historical order information within the target period to obtain preliminary historical orders;
根据搜索时间戳匹配产品使用时间，基于产品使用时间从初选历史订单匹配目标历史订单。Match product usage time according to the search timestamp, and match target historical orders from preliminary historical orders based on product usage time.
作为一种可选的实施方式，在本发明实施例第一方面中，分别计算目标历史订单中每一个历史订单的评分，包括：As an optional implementation manner, in the first aspect of the embodiment of the present invention, respectively calculating the score of each historical order in the target historical orders includes:
获取目标历史订单中的全部购买店铺，并统计每一个购买店铺的第一购买次数；Get all the purchase stores in the target's historical orders, and count the number of first purchases in each purchase store;
获取目标历史订单中的全部购买类目，并统计每一个购买类目的第二购买次数；Get all purchase categories in the target's historical orders and count the number of second purchases for each purchase category;
分别给不同数量的第一购买次数和第二购买次数对应的评分值；Give rating values corresponding to different numbers of first purchase times and second purchase times respectively;
获取每一个历史订单中的购买店铺对应的第一购买次数，以及根据每一个历史订单中的购买类目对应的第二购买次数，计算所述第一购买次数和第二购买次数分别对应的评分值之和以得到该历史订单的评分。Get the first purchase number corresponding to the purchase store in each historical order, and the second purchase number corresponding to the purchase category in each historical order, and calculate the sum of the score values corresponding to the first purchase number and the second purchase number respectively to obtain the score of the historical order.
作为一种可选的实施方式，在本发明实施例第一方面中，根据用户历史画像和关键字生成用户匹配的产品信息，包括：As an optional implementation manner, in the first aspect of the embodiment of the present invention, generating user-matched product information according to the user history portrait and keywords includes:
根据关键字从至少一个用户历史画像中选取匹配的用户历史画像；Selecting a matching user historical profile from at least one user historical profile according to the keyword;
获取所述匹配的用户历史画像，生成三维模拟人体形象，将与关键字匹配的同类目全部产品加载至三维模拟人体形象中，并计算每一种产品加载至三维模拟人体形象后的形象分值；Obtain the matched user history portrait, generate a three-dimensional simulated human image, load all products of the same category matching the keyword into the three-dimensional simulated human image, and calculate the image score of each product after being loaded into the three-dimensional simulated human image;
选取形象分值最高的一种产品为目标产品，获取该目标产品的细分词。Select a product with the highest image score as the target product, and obtain the segmentation words of the target product.
作为一种可选的实施方式，在本发明实施例第一方面中，将推荐产品生成推荐购买页面发送给用户，包括：As an optional implementation manner, in the first aspect of the embodiment of the present invention, generating a recommended purchase page for a recommended product and sending it to a user includes:
提取每一个推荐产品的主图和关键字，并获取太推荐产品的购买连接，将关键字嵌入至提取的主图中；Extract the main image and keywords of each recommended product, obtain the purchase link of the recommended product, and embed the keywords into the extracted main image;
将嵌入关键字的主图和购买链接生成新购买识别图；Generate a new purchase identification graph by embedding the main image with keywords and the purchase link;
从预设背景库中选取背景模板，将全部推荐产品的新购买识别图任意排列至所述背景模板中生成推荐购买页面，将所述购买页面发送给用户。A background template is selected from a preset background library, and new purchase identification images of all recommended products are arbitrarily arranged in the background template to generate a recommended purchase page, which is then sent to the user.
作为一种可选的实施方式，在本发明实施例第一方面中，将推荐产品生成推荐购买页面发送给用户，包括：As an optional implementation manner, in the first aspect of the embodiment of the present invention, generating a recommended purchase page for a recommended product and sending it to a user includes:
保留用户搜索关键字的搜索页面，并创建新的购买页面；Keep the search page for the user's search keyword and create a new purchase page;
将全部推荐产品随机排序添加至所述购买页面中，将该购买页面显示给用户。All recommended products are randomly sorted and added to the purchase page, and the purchase page is displayed to the user.
作为一种可选的实施方式，在本发明实施例第一方面中，还包括：As an optional implementation manner, in the first aspect of the embodiment of the present invention, it also includes:
接收用户同意或拒绝所述推荐购买页面的操作指令；Receive an operation instruction from the user to agree or reject the recommended purchase page;
当用户拒绝所述推荐购买页面时，关闭所述购买页面，并根据所述历史订单信息和历史画像信息生成用户购物偏好分值；When the user rejects the recommended purchase page, the purchase page is closed, and a user shopping preference score is generated according to the historical order information and historical portrait information;
根据所述用户购物偏好分值对搜索页面的产品按照所述用户购物偏好分值高低进行排序。According to the user shopping preference score, the products on the search page are sorted according to the user shopping preference score.
本发明实施例第二方面公开一种基于人工智能的电商产品导购装置，包括：A second aspect of an embodiment of the present invention discloses an e-commerce product shopping guide device based on artificial intelligence, comprising:
指令响应模块：用于响应于用户的搜索指令，获取所述搜索指令包含的搜索时间戳、关键字以及用户信息，所述用户信息包括账户信息和至少一个用户历史画像；所述用户历史画像包括面貌特征、身材信息、年龄信息、性别；Instruction response module: used to respond to the user's search instruction and obtain the search timestamp, keywords and user information contained in the search instruction, wherein the user information includes account information and at least one user history portrait; the user history portrait includes facial features, body shape information, age information and gender;
信息收集模块：用于基于账户信息获取用户的历史订单信息，并根据关键字从历史订单信息中筛选与关键字匹配的目标历史订单；Information collection module: used to obtain the user's historical order information based on the account information, and filter the target historical orders matching the keywords from the historical order information according to the keywords;
评分计算模块：用于分别计算目标历史订单中每一个历史订单的评分，选取评分在前N名的订单为参考订单；其中N为自然数，且N不等于0；Rating calculation module: used to calculate the rating of each historical order in the target historical orders respectively, and select the orders with the top N ratings as reference orders; where N is a natural number and N is not equal to 0;
产品推荐模块：用于根据用户历史画像和关键字生成用户匹配的产品信息，基于所述产品信息和参考订单生成推荐产品，将推荐产品生成推荐购买页面发送给用户。Product recommendation module: used to generate user-matching product information based on user historical portraits and keywords, generate recommended products based on the product information and reference orders, and generate a recommended purchase page for the recommended products and send it to the user.
作为一种可选的实施方式，在本发明实施例第二方面中，根据关键字从历史订单信息中筛选与关键字匹配的目标历史订单，包括：As an optional implementation, in the second aspect of the embodiment of the present invention, filtering the target historical orders matching the keyword from the historical order information according to the keyword includes:
删除历史订单信息中购物时间超过预设时间阈值的历史订单得到目标期限内的历史订单信息；Delete the historical orders whose purchase time exceeds the preset time threshold in the historical order information to obtain the historical order information within the target period;
获取关键字的产品类目，基于所述产品类目剔除目标期限内的历史订单信息中不匹配该产品类目的订单得到初选历史订单；Obtain the product category of the keyword, and based on the product category, remove orders that do not match the product category from historical order information within the target period to obtain preliminary historical orders;
根据搜索时间戳匹配产品使用时间，基于产品使用时间从初选历史订单匹配目标历史订单。Match product usage time according to the search timestamp, and match target historical orders from preliminary historical orders based on product usage time.
作为一种可选的实施方式，在本发明实施例第二方面中，分别计算目标历史订单中每一个历史订单的评分，包括：As an optional implementation, in the second aspect of the embodiment of the present invention, respectively calculating the score of each historical order in the target historical orders includes:
获取目标历史订单中的全部购买店铺，并统计每一个购买店铺的第一购买次数；Get all the purchase stores in the target's historical orders, and count the number of first purchases in each purchase store;
获取目标历史订单中的全部购买类目，并统计每一个购买类目的第二购买次数；Get all purchase categories in the target's historical orders and count the number of second purchases for each purchase category;
分别给不同数量的第一购买次数和第二购买次数对应的评分值；Give rating values corresponding to different numbers of first purchase times and second purchase times respectively;
获取每一个历史订单中的购买店铺对应的第一购买次数，以及根据每一个历史订单中的购买类目对应的第二购买次数，计算所述第一购买次数和第二购买次数分别对应的评分值之和以得到该历史订单的评分。Get the first purchase number corresponding to the purchase store in each historical order, and the second purchase number corresponding to the purchase category in each historical order, and calculate the sum of the score values corresponding to the first purchase number and the second purchase number respectively to obtain the score of the historical order.
作为一种可选的实施方式，在本发明实施例第二方面中，根据用户历史画像和关键字生成用户匹配的产品信息，包括：As an optional implementation, in the second aspect of the embodiment of the present invention, generating user-matched product information according to the user history portrait and keywords includes:
根据关键字从至少一个用户历史画像中选取匹配的用户历史画像；Selecting a matching user historical profile from at least one user historical profile according to the keyword;
获取所述匹配的用户历史画像，生成三维模拟人体形象，将与关键字匹配的同类目全部产品加载至三维模拟人体形象中，并计算每一种产品加载至三维模拟人体形象后的形象分值；Obtain the matched user history portrait, generate a three-dimensional simulated human image, load all products of the same category matching the keyword into the three-dimensional simulated human image, and calculate the image score of each product after being loaded into the three-dimensional simulated human image;
选取形象分值最高的一种产品为目标产品，获取该目标产品的细分词。Select a product with the highest image score as the target product, and obtain the segmentation words of the target product.
作为一种可选的实施方式，在本发明实施例第二方面中，将推荐产品生成推荐购买页面发送给用户，包括：As an optional implementation, in the second aspect of the embodiment of the present invention, generating a recommended purchase page for a recommended product and sending it to a user includes:
提取每一个推荐产品的主图和关键字，并获取太推荐产品的购买连接，将关键字嵌入至提取的主图中；Extract the main image and keywords of each recommended product, obtain the purchase link of the recommended product, and embed the keywords into the extracted main image;
将嵌入关键字的主图和购买链接生成新购买识别图；Generate a new purchase identification graph by embedding the main image with keywords and the purchase link;
从预设背景库中选取背景模板，将全部推荐产品的新购买识别图任意排列至所述背景模板中生成推荐购买页面，将所述购买页面发送给用户。A background template is selected from a preset background library, and new purchase identification images of all recommended products are arbitrarily arranged in the background template to generate a recommended purchase page, which is then sent to the user.
作为一种可选的实施方式，在本发明实施例第二方面中，将推荐产品生成推荐购买页面发送给用户，包括：As an optional implementation, in the second aspect of the embodiment of the present invention, generating a recommended purchase page for a recommended product and sending it to a user includes:
保留用户搜索关键字的搜索页面，并创建新的购买页面；Keep the search page for the user's search keyword and create a new purchase page;
将全部推荐产品随机排序添加至所述购买页面中，将该购买页面显示给用户。All recommended products are randomly sorted and added to the purchase page, and the purchase page is displayed to the user.
作为一种可选的实施方式，在本发明实施例第二方面中，还包括：As an optional implementation manner, in the second aspect of the embodiment of the present invention, it also includes:
接收用户同意或拒绝所述推荐购买页面的操作指令；Receive an operation instruction from the user to agree or reject the recommended purchase page;
当用户拒绝所述推荐购买页面时，关闭所述购买页面，并根据所述历史订单信息和历史画像信息生成用户购物偏好分值；When the user rejects the recommended purchase page, the purchase page is closed, and a user shopping preference score is generated according to the historical order information and historical portrait information;
根据所述用户购物偏好分值对搜索页面的产品按照所述用户购物偏好分值高低进行排序。According to the user shopping preference score, the products on the search page are sorted according to the user shopping preference score.
本发明实施例第三方面公开一种电子设备，包括：存储有可执行程序代码的存储器；与所述存储器耦合的处理器；所述处理器调用所述存储器中存储的所述可执行程序代码，用于执行本发明实施例第一方面公开的基于人工智能的电商产品导购方法。The third aspect of an embodiment of the present invention discloses an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the artificial intelligence-based e-commerce product shopping guide method disclosed in the first aspect of the embodiment of the present invention.
本发明实施例第四方面公开一种计算机可读存储介质，其存储计算机程序，其中，所述计算机程序使得计算机执行本发明实施例第一方面公开的基于人工智能的电商产品导购方法。A fourth aspect of an embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the artificial intelligence-based e-commerce product shopping guide method disclosed in the first aspect of an embodiment of the present invention.
与现有技术相比，本发明实施例具有以下有益效果：Compared with the prior art, the embodiments of the present invention have the following beneficial effects:
本发明实施例中公开的基于人工智能的电商产品导购方法包括响应于用户的搜索指令，获取所述搜索指令包含的搜索时间戳、关键字以及用户信息，基于账户信息获取用户的历史订单信息，并根据关键字从历史订单信息中筛选与关键字匹配的目标历史订单，分别计算目标历史订单中每一个历史订单的评分，选取评分在前N名的订单为参考订单，根据用户历史画像和关键字生成用户匹配的产品信息，基于所述产品信息和参考订单生成推荐产品，将推荐产品生成推荐购买页面发送给用户；实施例通过结合用户当前的购买需求以及用户的历史画像来自动匹配适合用户的产品，结合历史订单，从用户的购买偏好着手推荐，可以精准推荐更满足用户个性化需求的产品。The artificial intelligence-based e-commerce product shopping guide method disclosed in the embodiment of the present invention includes responding to a user's search instruction, obtaining a search timestamp, keywords and user information contained in the search instruction, obtaining the user's historical order information based on the account information, and screening the target historical orders matching the keywords from the historical order information according to the keywords, respectively calculating the scores of each historical order in the target historical orders, selecting the orders with the top N scores as reference orders, generating user-matched product information according to the user's historical portrait and keywords, generating recommended products based on the product information and the reference orders, and sending the recommended purchase page generated by the recommended products to the user; the embodiment automatically matches products suitable for the user by combining the user's current purchasing needs and the user's historical portrait, and recommends based on the user's purchasing preferences in combination with historical orders, so as to accurately recommend products that better meet the user's personalized needs.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
为了更清楚地说明本发明实施例中的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
图1是本发明实施例公开的一种基于人工智能的电商产品导购方法方法的流程示意图；FIG1 is a schematic diagram of a flow chart of an e-commerce product shopping guide method based on artificial intelligence disclosed in an embodiment of the present invention;
图2是本发明实施例提供的一种基于人工智能的电商产品导购方法装置的结构示意图；FIG2 is a schematic diagram of the structure of an e-commerce product shopping guide method and device based on artificial intelligence provided by an embodiment of the present invention;
图3是本发明实施例提供的一种电子设备的结构示意图。FIG. 3 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.
具体实施方式Detailed ways
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
需要说明的是，本发明的说明书和权利要求书中的术语“第一”、“第二”、“第三”、“第四”等是用于区别不同的对象，而不是用于描述特定顺序。本发明实施例的术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，示例性地，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。It should be noted that the terms "first", "second", "third", "fourth", etc. in the specification and claims of the present invention are used to distinguish different objects rather than to describe a specific order. The terms "including" and "having" in the embodiments of the present invention and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.
本发明实施例公开了基于人工智能的电商产品导购方法、装置、电子设备及存储介质，实施例中公开的基于人工智能的电商产品导购方法包括响应于用户的搜索指令，获取所述搜索指令包含的搜索时间戳、关键字以及用户信息，基于账户信息获取用户的历史订单信息，并根据关键字从历史订单信息中筛选与关键字匹配的目标历史订单，分别计算目标历史订单中每一个历史订单的评分，选取评分在前N名的订单为参考订单，根据用户历史画像和关键字生成用户匹配的产品信息，基于所述产品信息和参考订单生成推荐产品，将推荐产品生成推荐购买页面发送给用户；实施例通过结合用户当前的购买需求以及用户的历史画像来自动匹配适合用户的产品，结合历史订单，从用户的购买偏好着手推荐，可以精准推荐更满足用户个性化需求的产品。The embodiments of the present invention disclose an e-commerce product shopping guide method, device, electronic device and storage medium based on artificial intelligence. The e-commerce product shopping guide method based on artificial intelligence disclosed in the embodiments includes responding to a user's search instruction, obtaining a search timestamp, keywords and user information contained in the search instruction, obtaining the user's historical order information based on the account information, and screening target historical orders matching the keywords from the historical order information according to the keywords, respectively calculating the scores of each historical order in the target historical orders, selecting the orders with the top N scores as reference orders, generating user-matched product information according to the user's historical portrait and keywords, generating recommended products based on the product information and the reference orders, and sending the recommended purchase page generated by the recommended products to the user; the embodiments automatically match products suitable for the user by combining the user's current purchasing needs and the user's historical portrait, and recommend based on the user's purchasing preferences in combination with historical orders, so as to accurately recommend products that better meet the user's personalized needs.
实施例一Embodiment 1
请参阅图1，图1是本发明实施例公开的一种基于人工智能的电商产品导购方法的流程示意图。其中，本发明实施例所描述的方法的执行主体为由软件或/和硬件组成的执行主体，该执行主体可以通过有线或/和无线方式接收相关信息，并可以发送一定的指令。当然，其还可以具有一定的处理功能和存储功能。该执行主体可以控制多个设备，例如远程的物理服务器或云服务器以及相关软件，也可以是对某处安置的设备进行相关操作的本地主机或服务器以及相关软件等。在一些场景中，还可以控制多个存储设备，存储设备可以与设备放置于同一地方或不同地方。如图1所示，该基于人工智能的电商产品导购方法包括以下步骤：Please refer to Figure 1, which is a flow chart of an e-commerce product shopping guide method based on artificial intelligence disclosed in an embodiment of the present invention. Among them, the execution subject of the method described in the embodiment of the present invention is an execution subject composed of software and/or hardware, and the execution subject can receive relevant information by wired or/and wireless means, and can send certain instructions. Of course, it can also have certain processing functions and storage functions. The execution subject can control multiple devices, such as a remote physical server or cloud server and related software, or it can be a local host or server and related software that performs related operations on a device placed somewhere. In some scenarios, multiple storage devices can also be controlled, and the storage devices can be placed in the same place or different places as the devices. As shown in Figure 1, the e-commerce product shopping guide method based on artificial intelligence includes the following steps:
101、响应于用户的搜索指令，获取所述搜索指令包含的搜索时间戳、关键字以及用户信息。101. In response to a search instruction from a user, obtain a search timestamp, a keyword, and user information included in the search instruction.
实施例中，用户信息包括账户信息和至少一个用户历史画像；所述用户历史画像包括面貌特征、身材信息、年龄信息、性别。通常一个用户在某个购物平台登录自己的账号购物，可能是购买自己所需的物品，也可能给他人购买用户属性强的物品，例如衣服，则一个账户信息可能对应多个用户历史画像。实施例的用户历史画像是指在本次购物之前由用户人为输入，或者在人为输入的基础上结合对购物偏好生成的画像信息，例如用户的面貌特征可以由用户人为输入照片，或者用户输入描述特征的词句。In an embodiment, user information includes account information and at least one user history portrait; the user history portrait includes facial features, body information, age information, and gender. Usually, a user logs in to his or her account on a shopping platform to shop. He or she may buy items that he or she needs, or he or she may buy items with strong user attributes for others, such as clothes. In this case, one account information may correspond to multiple user history portraits. The user history portrait of an embodiment refers to the portrait information that is manually input by the user before the current shopping, or is generated based on the manual input and shopping preferences. For example, the user's facial features can be manually input by the user through photos, or the user can input words and sentences that describe the features.
102、基于账户信息获取用户的历史订单信息，并根据关键字从历史订单信息中筛选与关键字匹配的目标历史订单。102. Obtain the user's historical order information based on the account information, and filter the target historical orders matching the keyword from the historical order information according to the keyword.
本步骤中，从历史订单信息中进行筛选获取目标历史订单，具体的，删除历史订单信息中购物时间超过预设时间阈值的历史订单得到目标期限内的历史订单信息；获取关键字的产品类目，基于所述产品类目剔除目标期限内的历史订单信息中不匹配该产品类目的订单得到初选历史订单根据搜索时间戳匹配产品使用时间，基于产品使用时间从初选历史订单匹配目标历史订单。In this step, the historical order information is screened to obtain the target historical orders. Specifically, the historical orders whose shopping time exceeds the preset time threshold are deleted from the historical order information to obtain the historical order information within the target period; the product category of the keyword is obtained, and based on the product category, the orders that do not match the product category in the historical order information within the target period are eliminated to obtain the preliminary historical orders; the product usage time is matched according to the search timestamp, and the target historical orders are matched from the preliminary historical orders based on the product usage time.
上述中，例如预设时间阈值也即是根据目标期限进行限定，例如目标期限为3个月，也就是只选择以现在为时间起点过去三个月时间内的历史订单。可以更满足用户最近的购物爱好。In the above, for example, the preset time threshold is limited according to the target period, for example, the target period is 3 months, that is, only historical orders within the past three months starting from now are selected, which can better meet the user's recent shopping preferences.
103、分别计算目标历史订单中每一个历史订单的评分，选取评分在前N名的订单为参考订单；其中N为自然数，且N不等于0。103. Calculate the score of each historical order in the target historical orders respectively, and select the orders with the top N scores as reference orders; where N is a natural number, and N is not equal to 0.
本步骤中，获取目标历史订单中的全部购买店铺，并统计每一个购买店铺的第一购买次数；获取目标历史订单中的全部购买类目，并统计每一个购买类目的第二购买次数；分别给不同数量的第一购买次数和第二购买次数对应的评分值；获取每一个历史订单中的购买店铺对应的第一购买次数，以及根据每一个历史订单中的购买类目对应的第二购买次数，计算所述第一购买次数和第二购买次数分别对应的评分值之和以得到该历史订单的评分。In this step, all the purchasing stores in the target historical orders are obtained, and the number of first purchases in each purchasing store is counted; all the purchasing categories in the target historical orders are obtained, and the number of second purchases in each purchasing category is counted; scoring values are respectively given to different numbers of first purchases and second purchases; the number of first purchases corresponding to the purchasing stores in each historical order is obtained, and based on the second purchases corresponding to the purchasing categories in each historical order, the sum of the scoring values corresponding to the first purchases and the second purchases is calculated to obtain the score of the historical order.
示例性的，用户的目标历史订单中，购买类目包括有服饰、办公用品、厨房用品，服饰的购买次数、办公用品的购买次数以及厨房用品的购买次数均属于第一购买次数，进一步以服饰为例，其购买服饰的店铺总共有A店铺、B店铺、C店铺，并且A店铺购买3次，B店铺购买1次，C店铺购买2次，则这些店铺的购买次数为第二购买次数，提前设定例如同一个店铺购买1次对应x1评分值，购买2次对应x2评分值，购买3次对应x3评分值……，第一购买次数同理，x1、x2和x3均指代具体的数值。Exemplarily, in the user's target historical orders, the purchase categories include clothing, office supplies, and kitchen supplies. The purchase times of clothing, office supplies, and kitchen supplies all belong to the first purchase times. Taking clothing as an example, the stores where the clothing was purchased are store A, store B, and store C. There were 3 purchases at store A, 1 purchase at store B, and 2 purchases at store C. The purchase times at these stores are the second purchase times. For example, it is set in advance that a purchase at the same store once corresponds to a score value of x1, 2 purchases correspond to a score value of x2, 3 purchases correspond to a score value of x3… The same is true for the first purchase times. x1, x2, and x3 all refer to specific values.
104、根据用户历史画像和关键字生成用户匹配的产品信息，基于所述产品信息和参考订单生成推荐产品，将推荐产品生成推荐购买页面发送给用户。104. Generate user-matching product information based on the user's historical portrait and keywords, generate recommended products based on the product information and reference orders, and generate a recommended purchase page for the recommended products and send it to the user.
本步骤中，根据关键字从至少一个用户历史画像中选取匹配的用户历史画像；获取所述匹配的用户历史画像，生成三维模拟人体形象，将与关键字匹配的同类目全部产品加载至三维模拟人体形象中，并计算每一种产品加载至三维模拟人体形象后的形象分值；选取形象分值最高的一种产品为目标产品，获取该目标产品的细分词。In this step, a matching user historical portrait is selected from at least one user historical portrait based on the keyword; the matching user historical portrait is obtained, a three-dimensional simulated human image is generated, all products of the same category matching the keyword are loaded into the three-dimensional simulated human image, and the image score of each product after being loaded into the three-dimensional simulated human image is calculated; the product with the highest image score is selected as the target product, and the segmentation words of the target product are obtained.
三维模拟人体形象是基于用户历史画像生成的符合用户历史画像形象的三维人体模型，例如用户历史画像中用户身高160cm，腰围60cm，则绘制的三维人体模型也是对应身高160cm的比例，腰围60cm的比例，例如当用户挑选衣服、包包等可根据用户的整体形象进行考量。而计算形象分值可以根据提前设定的规格，例如修身上衣的下沿应当到身体哪个位置范围，在范围内该项为满分，超过范围多少会对应扣多少分。The 3D simulated human image is a 3D human model that is generated based on the user's historical portrait and conforms to the user's historical portrait image. For example, in the user's historical portrait, the user's height is 160cm and the waist is 60cm. The 3D human model drawn is also in the proportion of 160cm height and 60cm waist. For example, when the user chooses clothes and bags, they can consider the user's overall image. The image score can be calculated according to the pre-set specifications, such as the range of the body where the bottom edge of a slim-fitting top should be. If it is within the range, the item will be full marks, and if it exceeds the range, the corresponding number of points will be deducted.
进一步的，将推荐产品生成推荐购买页面发送给用户，包括：提取每一个推荐产品的主图和关键字，并获取太推荐产品的购买连接，将关键字嵌入至提取的主图中；将嵌入关键字的主图和购买链接生成新购买识别图；从预设背景库中选取背景模板，将全部推荐产品的新购买识别图任意排列至所述背景模板中生成推荐购买页面，将所述购买页面发送给用户。Furthermore, a recommended purchase page is generated from the recommended products and sent to the user, including: extracting the main image and keywords of each recommended product, obtaining the purchase link of the recommended product, and embedding the keywords into the extracted main image; generating a new purchase identification image from the main image and the purchase link with the embedded keywords; selecting a background template from a preset background library, arbitrarily arranging the new purchase identification images of all recommended products into the background template to generate a recommended purchase page, and sending the purchase page to the user.
另一个示例中，还可以是保留用户搜索关键字的搜索页面，并创建新的购买页面；将全部推荐产品随机排序添加至所述购买页面中，将该购买页面显示给用户。In another example, the search page for the user's search keyword may be retained, and a new purchase page may be created; all recommended products may be added to the purchase page in random order, and the purchase page may be displayed to the user.
本实施例中，用户也可以拒绝购物推荐继续按照自己不确定的或者新的喜好进行搜索，则接收用户同意或拒绝所述推荐购买页面的操作指令；当用户拒绝所述推荐购买页面时，关闭所述购买页面，并根据所述历史订单信息和历史画像信息生成用户购物偏好分值；根据所述用户购物偏好分值对搜索页面的产品按照所述用户购物偏好分值高低进行排序。In this embodiment, the user may also reject the shopping recommendation and continue searching according to his or her uncertain or new preferences, and then receive the user's operation instruction to agree or reject the recommended purchase page; when the user rejects the recommended purchase page, the purchase page is closed, and a user shopping preference score is generated based on the historical order information and historical portrait information; and the products on the search page are sorted according to the user shopping preference score.
实施例二Embodiment 2
请参阅图2，图2是本发明实施例公开的基于人工智能的电商产品导购装置的结构示意图。如图2所示，该基于人工智能的电商产品导购装置可以包括：指令响应模块201、信息收集模块202、评分计算模块203和产品推荐模块204，其中，指令响应模块201：用于响应于用户的搜索指令，获取所述搜索指令包含的搜索时间戳、关键字以及用户信息，所述用户信息包括账户信息和至少一个用户历史画像；所述用户历史画像包括面貌特征、身材信息、年龄信息、性别；信息收集模块202：用于基于账户信息获取用户的历史订单信息，并根据关键字从历史订单信息中筛选与关键字匹配的目标历史订单；评分计算模块203：用于分别计算目标历史订单中每一个历史订单的评分，选取评分在前N名的订单为参考订单；其中N为自然数，且N不等于0；产品推荐模块204：用于根据用户历史画像和关键字生成用户匹配的产品信息，基于所述产品信息和参考订单生成推荐产品，将推荐产品生成推荐购买页面发送给用户。Please refer to Figure 2, which is a schematic diagram of the structure of an e-commerce product shopping guide device based on artificial intelligence disclosed in an embodiment of the present invention. As shown in Figure 2, the e-commerce product shopping guide device based on artificial intelligence may include: an instruction response module 201, an information collection module 202, a score calculation module 203 and a product recommendation module 204, wherein the instruction response module 201: is used to respond to the user's search instruction, obtain the search timestamp, keywords and user information contained in the search instruction, and the user information includes account information and at least one user history portrait; the user history portrait includes facial features, body information, age information, and gender; the information collection module 202: is used to obtain the user's historical order information based on the account information, and filter the target historical orders matching the keywords from the historical order information according to the keywords; the score calculation module 203: is used to calculate the score of each historical order in the target historical order respectively, and select the orders with the top N scores as reference orders; wherein N is a natural number, and N is not equal to 0; the product recommendation module 204: is used to generate user-matched product information according to the user history portrait and keywords, generate recommended products based on the product information and reference orders, and generate a recommended purchase page for the recommended products and send it to the user.
信息收集模块202中，根据关键字从历史订单信息中筛选与关键字匹配的目标历史订单，包括：删除历史订单信息中购物时间超过预设时间阈值的历史订单得到目标期限内的历史订单信息；获取关键字的产品类目，基于所述产品类目剔除目标期限内的历史订单信息中不匹配该产品类目的订单得到初选历史订单；根据搜索时间戳匹配产品使用时间，基于产品使用时间从初选历史订单匹配目标历史订单。In the information collection module 202, target historical orders matching the keywords are screened from the historical order information according to the keywords, including: deleting historical orders whose shopping time exceeds a preset time threshold in the historical order information to obtain historical order information within the target period; obtaining the product category of the keyword, and based on the product category, eliminating orders that do not match the product category in the historical order information within the target period to obtain preliminary historical orders; matching the product usage time according to the search timestamp, and matching the target historical orders from the preliminary historical orders based on the product usage time.
评分计算模块203中，分别计算目标历史订单中每一个历史订单的评分，包括：获取目标历史订单中的全部购买店铺，并统计每一个购买店铺的第一购买次数；获取目标历史订单中的全部购买类目，并统计每一个购买类目的第二购买次数；分别给不同数量的第一购买次数和第二购买次数对应的评分值；获取每一个历史订单中的购买店铺对应的第一购买次数，以及根据每一个历史订单中的购买类目对应的第二购买次数，计算所述第一购买次数和第二购买次数分别对应的评分值之和以得到该历史订单的评分。In the score calculation module 203, the score of each historical order in the target historical orders is calculated respectively, including: obtaining all the purchasing stores in the target historical orders, and counting the first purchase times of each purchasing store; obtaining all the purchasing categories in the target historical orders, and counting the second purchase times of each purchasing category; giving score values corresponding to different numbers of first purchase times and second purchase times respectively; obtaining the first purchase times corresponding to the purchasing stores in each historical order, and according to the second purchase times corresponding to the purchasing categories in each historical order, calculating the sum of the score values corresponding to the first purchase times and the second purchase times respectively to obtain the score of the historical order.
产品推荐模块204中，根据用户历史画像和关键字生成用户匹配的产品信息，包括：根据关键字从至少一个用户历史画像中选取匹配的用户历史画像；获取所述匹配的用户历史画像，生成三维模拟人体形象，将与关键字匹配的同类目全部产品加载至三维模拟人体形象中，并计算每一种产品加载至三维模拟人体形象后的形象分值；选取形象分值最高的一种产品为目标产品，获取该目标产品的细分词。In the product recommendation module 204, user-matched product information is generated based on the user historical portrait and keywords, including: selecting a matching user historical portrait from at least one user historical portrait based on the keyword; obtaining the matching user historical portrait, generating a three-dimensional simulated human image, loading all products of the same category matching the keyword into the three-dimensional simulated human image, and calculating the image score of each product after being loaded into the three-dimensional simulated human image; selecting a product with the highest image score as the target product, and obtaining the subdivision words of the target product.
产品推荐模块204中将推荐产品生成推荐购买页面发送给用户，示例性可以包括提取每一个推荐产品的主图和关键字，并获取太推荐产品的购买连接，将关键字嵌入至提取的主图中；将嵌入关键字的主图和购买链接生成新购买识别图；从预设背景库中选取背景模板，将全部推荐产品的新购买识别图任意排列至所述背景模板中生成推荐购买页面，将所述购买页面发送给用户。另一个示例中，还可以包括保留用户搜索关键字的搜索页面，并创建新的购买页面；将全部推荐产品随机排序添加至所述购买页面中，将该购买页面显示给用户。The product recommendation module 204 generates a recommended purchase page for the recommended products and sends it to the user. For example, it may include extracting the main image and keywords of each recommended product, obtaining the purchase link of the recommended product, embedding the keyword into the extracted main image; generating a new purchase identification image from the main image and purchase link embedded with the keyword; selecting a background template from a preset background library, arbitrarily arranging the new purchase identification images of all recommended products into the background template to generate a recommended purchase page, and sending the purchase page to the user. In another example, it may also include retaining a search page for the user's search keyword and creating a new purchase page; randomly sorting all recommended products and adding them to the purchase page, and displaying the purchase page to the user.
实施例还可以包括拒绝处理模块，用于接收用户同意或拒绝所述推荐购买页面的操作指令；当用户拒绝所述推荐购买页面时，关闭所述购买页面，并根据所述历史订单信息和历史画像信息生成用户购物偏好分值；根据所述用户购物偏好分值对搜索页面的产品按照所述用户购物偏好分值高低进行排序。The embodiment may also include a rejection processing module for receiving an operation instruction from a user to agree or reject the recommended purchase page; when the user rejects the recommended purchase page, closing the purchase page and generating a user shopping preference score based on the historical order information and historical portrait information; and sorting the products on the search page according to the user shopping preference score.
实施例三Embodiment 3
请参阅图3，图3是本发明实施例公开的一种电子设备的结构示意图。电子设备可以是计算机以及服务器等，当然，在一定情况下，还可以是手机、平板电脑以及监控终端等智能设备，以及具有处理功能的图像采集装置。如图3所示，该电子设备可以包括：Please refer to FIG3, which is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. The electronic device may be a computer, a server, etc. Of course, in certain circumstances, it may also be a smart device such as a mobile phone, a tablet computer, and a monitoring terminal, as well as an image acquisition device with processing functions. As shown in FIG3, the electronic device may include:
存储有可执行程序代码的存储器301；A memory 301 storing executable program codes;
与存储器301耦合的处理器302；a processor 302 coupled to the memory 301;
其中，处理器302调用存储器301中存储的可执行程序代码，执行实施例一中的基于人工智能的电商产品导购方法中的部分或全部步骤。The processor 302 calls the executable program code stored in the memory 301 to execute part or all of the steps in the artificial intelligence-based e-commerce product shopping guide method in the first embodiment.
本发明实施例公开一种计算机可读存储介质，其存储计算机程序，其中，该计算机程序使得计算机执行实施例一中的基于人工智能的电商产品导购方法中的部分或全部步骤。An embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute part or all of the steps in the artificial intelligence-based e-commerce product shopping guide method in Embodiment 1.
本发明实施例还公开一种计算机程序产品，其中，当计算机程序产品在计算机上运行时，使得计算机执行实施例一中的基于人工智能的电商产品导购方法中的部分或全部步骤。An embodiment of the present invention further discloses a computer program product, wherein when the computer program product runs on a computer, the computer executes part or all of the steps in the artificial intelligence-based e-commerce product shopping guide method in Embodiment 1.
本发明实施例还公开一种应用发布平台，其中，应用发布平台用于发布计算机程序产品，其中，当计算机程序产品在计算机上运行时，使得计算机执行实施例一中的基于人工智能的电商产品导购方法中的部分或全部步骤。An embodiment of the present invention further discloses an application publishing platform, wherein the application publishing platform is used to publish a computer program product, wherein when the computer program product runs on a computer, the computer executes part or all of the steps in the artificial intelligence-based e-commerce product shopping guide method in embodiment one.
在本发明的各种实施例中，应理解，所述各过程的序号的大小并不意味着执行顺序的必然先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本发明实施例的实施过程构成任何限定。In various embodiments of the present invention, it should be understood that the size of the serial numbers of the processes does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
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单元，即可位于一个地方，或者也可以分布到多个网络单元上。可根据实际的需要选择其中的部分或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, i.e., they may be located in one place or distributed over multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.
另外，在本发明各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。所述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。In addition, each functional unit in each embodiment of the present invention 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. The integrated unit may be implemented in the form of hardware or in the form of software functional units.
所述集成的单元若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，可以存储在一个计算机可获取的存储器中。基于这样的理解，本发明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全部或者部分，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器中，包括若干请求用以使得一台计算机设备(可以为个人计算机、服务器或者网络设备等，具体可以是计算机设备中的处理器)执行本发明的各个实施例所述方法的部分或全部步骤。If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several requests for a computer device (which can be a personal computer, a server or a network device, etc., specifically a processor in a computer device) to perform some or all of the steps of the method described in each embodiment of the present invention.
在本发明所提供的实施例中，应理解，“与A对应的B”表示B与A相关联，根据A可以确定B。但还应理解，根据A确定B并不意味着仅仅根据A确定B，还可以根据A和/或其他信息确定B。In the embodiments provided by the present invention, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and/or other information.
本领域普通技术人员可以理解所述实施例的各种方法中的部分或全部步骤是可以通过程序来指令相关的硬件来完成，该程序可以存储于一计算机可读存储介质中，存储介质包括只读存储器(Read-Only Memory，ROM)、随机存储器(Random Access Memory，RAM)、可编程只读存储器(Programmable Read-only Memory，PROM)、可擦除可编程只读存储器(Erasable Programmable Read-Only Memory，EPROM)、一次可编程只读存储器(One-time Programmable Read-Only Memory，OTPROM)、电子抹除式可复写只读存储器(Electrically-Erasable Programmable Read-Only Memory，EEPROM)、只读光盘(CompactDisc Read-Only Memory，CD-ROM)或其他光盘存储器、磁盘存储器、磁带存储器、或者能够用于携带或存储数据的计算机可读的任何其他介质。A person of ordinary skill in the art can understand that some or all of the steps in the various methods of the embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.
以上对本发明实施例公开的基于人工智能的电商产品导购方法、装置、电子设备及存储介质进行了详细介绍，本文中应用了具体个例对本发明的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本发明的方法及其核心思想；同时，对于本领域的一般技术人员，依据本发明的思想，在具体实施方式及应用范围上均会有改变之处，综上所述，本说明书内容不应理解为对本发明的限制。The above is a detailed introduction to the artificial intelligence-based e-commerce product shopping guide method, device, electronic device and storage medium disclosed in the embodiments of the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the present invention.
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410563448.8ACN118333655B (en) | 2024-05-08 | 2024-05-08 | An e-commerce product shopping guide method and device based on artificial intelligence |
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410563448.8ACN118333655B (en) | 2024-05-08 | 2024-05-08 | An e-commerce product shopping guide method and device based on artificial intelligence |
| Publication Number | Publication Date |
|---|---|
| CN118333655Atrue CN118333655A (en) | 2024-07-12 |
| CN118333655B CN118333655B (en) | 2025-06-20 |
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410563448.8AActiveCN118333655B (en) | 2024-05-08 | 2024-05-08 | An e-commerce product shopping guide method and device based on artificial intelligence |
| Country | Link |
|---|---|
| CN (1) | CN118333655B (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119205270A (en)* | 2024-10-11 | 2024-12-27 | 苏州市世为科技有限公司 | Man-machine interaction method, device, system and storage medium |
| CN120278796A (en)* | 2025-06-09 | 2025-07-08 | 浪潮智慧供应链科技(山东)有限公司 | AI search recommendation method and system for purchasing |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160350832A1 (en)* | 2015-05-29 | 2016-12-01 | Ahold Licensing Sàrl | Method and system for automatically generating recommendations for a client shopping list |
| CN107730337A (en)* | 2016-08-12 | 2018-02-23 | 北京京东尚科信息技术有限公司 | Information-pushing method and device |
| CN107845005A (en)* | 2016-09-19 | 2018-03-27 | 北京京东尚科信息技术有限公司 | webpage generating method and device |
| CN111104590A (en)* | 2019-11-19 | 2020-05-05 | 泰康保险集团股份有限公司 | Information recommendation method, device, medium and electronic equipment |
| JP2020129262A (en)* | 2019-02-08 | 2020-08-27 | Ｎｅｃプラットフォームズ株式会社 | Order entry system, mobile terminal, terminal equipped with table, pos terminal, and ordering method |
| CN111681070A (en)* | 2020-05-25 | 2020-09-18 | 深圳市商汤科技有限公司 | Online product purchase method, purchase device, storage device, and purchase device |
| CN112801761A (en)* | 2021-04-13 | 2021-05-14 | 中智关爱通(南京)信息科技有限公司 | Commodity recommendation method, computing device and computer-readable storage medium |
| CN113706260A (en)* | 2021-09-01 | 2021-11-26 | 镇江纵陌阡横信息科技有限公司 | E-commerce platform commodity recommendation method and device based on search content |
| KR20220128145A (en)* | 2021-03-12 | 2022-09-20 | 주식회사 퓨처키친 | Food recommendation method using deep learning module |
| CN117314558A (en)* | 2023-09-20 | 2023-12-29 | 中国工商银行股份有限公司 | Product recommendation method and device, electronic equipment and storage medium |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160350832A1 (en)* | 2015-05-29 | 2016-12-01 | Ahold Licensing Sàrl | Method and system for automatically generating recommendations for a client shopping list |
| CN107730337A (en)* | 2016-08-12 | 2018-02-23 | 北京京东尚科信息技术有限公司 | Information-pushing method and device |
| CN107845005A (en)* | 2016-09-19 | 2018-03-27 | 北京京东尚科信息技术有限公司 | webpage generating method and device |
| JP2020129262A (en)* | 2019-02-08 | 2020-08-27 | Ｎｅｃプラットフォームズ株式会社 | Order entry system, mobile terminal, terminal equipped with table, pos terminal, and ordering method |
| CN111104590A (en)* | 2019-11-19 | 2020-05-05 | 泰康保险集团股份有限公司 | Information recommendation method, device, medium and electronic equipment |
| CN111681070A (en)* | 2020-05-25 | 2020-09-18 | 深圳市商汤科技有限公司 | Online product purchase method, purchase device, storage device, and purchase device |
| KR20220128145A (en)* | 2021-03-12 | 2022-09-20 | 주식회사 퓨처키친 | Food recommendation method using deep learning module |
| CN112801761A (en)* | 2021-04-13 | 2021-05-14 | 中智关爱通(南京)信息科技有限公司 | Commodity recommendation method, computing device and computer-readable storage medium |
| CN113706260A (en)* | 2021-09-01 | 2021-11-26 | 镇江纵陌阡横信息科技有限公司 | E-commerce platform commodity recommendation method and device based on search content |
| CN117314558A (en)* | 2023-09-20 | 2023-12-29 | 中国工商银行股份有限公司 | Product recommendation method and device, electronic equipment and storage medium |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119205270A (en)* | 2024-10-11 | 2024-12-27 | 苏州市世为科技有限公司 | Man-machine interaction method, device, system and storage medium |
| CN120278796A (en)* | 2025-06-09 | 2025-07-08 | 浪潮智慧供应链科技(山东)有限公司 | AI search recommendation method and system for purchasing |
| Publication number | Publication date |
|---|---|
| CN118333655B (en) | 2025-06-20 |
| Publication | Publication Date | Title |
|---|---|---|
| CN118333655B (en) | An e-commerce product shopping guide method and device based on artificial intelligence | |
| CN109492180A (en) | Resource recommendation method, device, computer equipment and computer readable storage medium | |
| CN112288517A (en) | Commodity recommendation method and device combining RPA and AI | |
| CN114862516A (en) | Copywriting recommendation method, storage medium and program product | |
| CN108509458B (en) | Business object identification method and device | |
| CN111784449A (en) | Data pushing method, data pushing equipment, storage medium and device | |
| CN116308684B (en) | Online shopping platform store information pushing method and system | |
| JP2020042676A (en) | Information processing apparatus and information processing method | |
| KR20150090353A (en) | Clothing matching service method | |
| CN112765230A (en) | Payment big data analysis method and big data analysis system based on internet finance | |
| CN113327132A (en) | Multimedia recommendation method, device, equipment and storage medium | |
| WO2024245068A1 (en) | Commodity review content processing method, and electronic device | |
| CN117745373A (en) | Product recommendation method and system and electronic equipment | |
| CN113988978A (en) | Garment matching recommendation method, system and equipment based on knowledge graph | |
| CN113781171A (en) | Information pushing method, device, equipment and storage medium | |
| CN112307231B (en) | Information processing, model training, feature library creation method and electronic equipment | |
| WO2024178771A1 (en) | Personalized dish seasoning method and apparatus based on food ordering habits | |
| CN111046285A (en) | A method, device, server and storage medium for determining recommendation ranking | |
| CN106294361B (en) | Information providing method and information providing system | |
| CN106157054A (en) | A kind of transaction data processing method and device | |
| CN107632989B (en) | Method and device for selecting commodity objects, determining models and determining use heat | |
| CN110189188B (en) | Commodity management method, commodity management device, computer equipment and storage medium | |
| CN118445494A (en) | Collaborative recommendation method, device, equipment and medium based on attribute graph model | |
| CN118569954A (en) | Clothing supply chain risk analysis method and device | |
| CN108345600A (en) | A kind of management, data search method and its device of search application |
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |