技术领域technical field
本发明涉及互联网技术领域，尤其涉及社交网络中推荐好友的方法及系统。The invention relates to the technical field of the Internet, in particular to a method and a system for recommending friends in a social network.
背景技术Background technique
随着互联网技术的飞速发展，出现了微博、校内网、Facebook等多种多样的社交网络，通常用户在注册这些社交网络时需要填写个人信息，包括居住地、学校、个人性格、爱好等信息。现有社交网络的好友推荐功能大多都是基于用户注册时输入的个人信息进行相关度匹配，以推荐相同爱好、相同城市、或者相同学校的其他用户。然而很多用户在填写这些个人信息时很随意，或者懒于填写，或者故意避其短展其长，导致得到的用户信息不真实不全面，导致社交网络的好友推荐不准确；另一方面，用户的个人信息可能会随着时间的变化发生变化，例如居住地变化、爱好变化等，进一步影响好友推荐的准确性。With the rapid development of Internet technology, various social networks such as Weibo, Xiaonei, and Facebook have emerged. Usually, users need to fill in personal information when registering these social networks, including information such as residence, school, personal personality, hobbies, etc. . Most of the friend recommendation functions of existing social networks are based on the personal information entered by the user during registration for correlation matching to recommend other users with the same hobbies, the same city, or the same school. However, many users are very casual when filling in these personal information, or are lazy to fill in, or deliberately avoid short and long, resulting in untrue and incomplete user information, resulting in inaccurate friend recommendations on social networks; on the other hand, users Your personal information may change over time, such as changes in residence, changes in hobbies, etc., which will further affect the accuracy of friend recommendations.
发明内容Contents of the invention
本发明的目的在于提出社交网络好友推荐的方法及系统，提高了社交网络中用户个人信息的真实性，提高好友匹配的准确性。The purpose of the present invention is to propose a method and system for friend recommendation in a social network, which improves the authenticity of user personal information in the social network and improves the accuracy of friend matching.
为达此目的，本发明采用以下技术方案：For reaching this purpose, the present invention adopts following technical scheme:
一种社交网络中推荐好友的方法，包括：A method for recommending friends in a social network, comprising:
采集用户的行为特征数据；Collect user behavior characteristic data;
发送所述行为特征数据到社交网络；Send the behavior characteristic data to social network;
分析所述行为特征数据得出该用户的个人信息；Analyze the behavioral characteristic data to obtain the user's personal information;
比对该用户与其他社交网络用户的个人信息，找出与该用户的个人信息匹配的社交网络用户。Comparing the personal information of the user with other social network users to find out the social network users matching the user's personal information.
优选的，所述特征数据包括：用户出入的场所类别信息、运动信息和日常起居信息，其中，所述运动信息包括运动时间、运动强度及运动频率，所述日常起居信息包括心率信息；Preferably, the feature data includes: information on the type of places the user enters and exits, exercise information and daily life information, wherein the exercise information includes exercise time, exercise intensity and exercise frequency, and the daily life information includes heart rate information;
所述分析所述行为特征数据得出该用户的个人信息，包括：The personal information of the user is obtained by analyzing the behavioral characteristic data, including:
分析所述场所类别信息得出该用户的兴趣爱好类型；Analyzing the venue category information to obtain the user's hobbies and hobbies;
分析所述运动信息得出该用户偏好的运动类型；Analyzing the exercise information to obtain the user's preferred exercise type;
分析所述日常起居信息得出该用户的日常作息规律。The daily routine of the user is obtained by analyzing the daily living information.
优选的，所述采集用户的行为特征数据，包括：Preferably, the collection of user behavior characteristic data includes:
按照第一设定时间周期采集用户的行为特征数据；Collecting user behavior characteristic data according to a first set time period;
所述发送所述行为特征数据到社交网络，包括：The sending the behavior characteristic data to the social network includes:
按照第二设定时间周期上传该用户的所述个人信息到社交网络；uploading the personal information of the user to a social network according to a second set time period;
所述第二设定时间周期大于等于所述第一设定时间周期。The second set time period is greater than or equal to the first set time period.
优选的，所述发送所述行为特征数据到社交网络之前，包括：Preferably, before sending the behavior characteristic data to the social network, it includes:
对行为特征数据上传社交网络进行授权。Authorize behavioral feature data to be uploaded to social networks.
优选的，所述发送所述行为特征数据到社交网络，包括：Preferably, the sending of the behavior characteristic data to a social network includes:
上传所述行为特征数据到预设服务器，由所述预设服务器转发所述行为特征数据。uploading the behavior feature data to a preset server, and the preset server forwards the behavior feature data.
优选的，所述分析所述行为特征数据得出该用户的个人信息之后，还包括：Preferably, after analyzing the behavior characteristic data to obtain the user's personal information, it further includes:
标记所述个人信息的类型为真实数据。Mark the type of the personal information as real data.
优选的，所述比对该用户与其他社交网络用户的个人信息，找出与该用户的个人信息匹配的社交网络用户，包括：Preferably, said comparing the personal information of the user with other social network users to find out the social network user matching the user's personal information includes:
比对该用户与其他社交网络用户的个人信息，得出其他社交网络用户的个人信息与该用户的个人信息的匹配度；Compare the personal information of the user with that of other social network users to find out the matching degree between the personal information of other social network users and the personal information of the user;
按所述匹配度由高到低进行排序，找出排在前面的预设数量的社交网络用户为该用户的推荐好友；Sorting according to the degree of matching from high to low, finding out a preset number of social network users in the front as the recommended friends of the user;
将所述推荐好友发送给所述用户。Sending the recommended friend to the user.
优选的，所述比对该用户与其他社交网络用户的个人信息，找出与该用户的个人信息匹配的社交网络用户，包括：Preferably, said comparing the personal information of the user with other social network users to find out the social network user matching the user's personal information includes:
接收设定的好友匹配条件；Receive the set friend matching conditions;
比对该用户与其他社交网络用户的个人信息，得出所述好友匹配条件下其他社交网络用户与该用户的匹配度；Comparing the personal information of the user with other social network users to obtain the matching degree between other social network users and the user under the friend matching condition;
按所述匹配度由高到低进行排序，找出排在前面的预设数量的社交网络用户为该用户的推荐好友；Sorting according to the degree of matching from high to low, finding out a preset number of social network users in the front as the recommended friends of the user;
将所述推荐好友发送给所述用户。Sending the recommended friend to the user.
本发明另一方面还提供一种社交网络中推荐好友的系统，包括：智能穿戴设备、与所述智能穿戴设备通信的社交网络服务器，所述智能穿戴设备包括数据采集单元和数据上传单元，所述社交网络服务器包括分析单元和匹配单元；Another aspect of the present invention also provides a system for recommending friends in a social network, including: a smart wearable device, a social network server communicating with the smart wearable device, the smart wearable device includes a data collection unit and a data upload unit, so The social networking server includes an analyzing unit and a matching unit;
所述数据采集单元，用于采集用户的行为特征数据；The data collection unit is used to collect behavior characteristic data of users;
所述数据上传单元，用于发送所述行为特征数据到所述社交网络服务器；The data uploading unit is configured to send the behavior characteristic data to the social networking server;
所述分析单元，用于分析所述行为特征数据得出该用户的个人信息；The analysis unit is configured to analyze the behavior characteristic data to obtain the user's personal information;
所述匹配单元，用于比对该用户与其他社交网络用户的个人信息，找出与该用户的个人信息匹配的社交网络用户。The matching unit is configured to compare the personal information of the user with other social network users, and find a social network user matching the user's personal information.
优选的，所述特征数据包括：用户出入的场所类别信息、运动信息和日常起居信息，其中，所述运动信息包括运动时间、运动强度及运动频率，所述日常起居信息包括心率；Preferably, the feature data includes: information on the category of places the user enters and exits, exercise information and daily life information, wherein the exercise information includes exercise time, exercise intensity and exercise frequency, and the daily life information includes heart rate;
所述分析单元，具体用于分析所述场所类别信息得出该用户的兴趣爱好类型；分析所述运动信息得出该用户的运动类型；分析所述日常起居信息得出该用户的日常作息规律。The analysis unit is specifically used to analyze the location category information to obtain the user's hobby type; analyze the exercise information to obtain the user's exercise type; analyze the daily life information to obtain the user's daily routine .
优选的，所述数据采集单元，具体用于按照第一设定时间周期采集用户的行为特征数据；Preferably, the data collection unit is specifically configured to collect user behavior characteristic data according to a first set time period;
所述数据上传单元，具体用于按照第二设定时间周期上传该用户的所述个人信息到所述社交网络服务器；The data uploading unit is specifically configured to upload the personal information of the user to the social networking server according to a second set time period;
其中，所述第二设定时间周期大于等于所述第一设定时间周期。Wherein, the second set time period is greater than or equal to the first set time period.
优选的，所述智能穿戴设备还包括授权单元，用于发送所述行为特征数据到社交网络之前，对行为特征数据上传社交网络进行授权。Preferably, the smart wearable device further includes an authorization unit, configured to authorize the uploading of the behavior characteristic data to the social network before sending the behavior characteristic data to the social network.
优选的，所述数据上传单元，具体用于上传所述行为特征数据到预设服务器，所述社交网络服务器向所述预设服务器请求获取所述行为特征数据。Preferably, the data uploading unit is specifically configured to upload the behavior characteristic data to a preset server, and the social network server requests the preset server to obtain the behavior characteristic data.
优选的，所述分析单元，还用于在分析所述行为特征数据得出该用户的个人信息之后，标记所述个人信息的类型为真实数据。Preferably, the analysis unit is further configured to mark the type of the personal information as real data after analyzing the behavior characteristic data to obtain the user's personal information.
优选的，所述匹配单元，具体用于比对该用户与其他社交网络用户的个人信息，得出其他社交网络用户的个人信息与该用户的个人信息的匹配度；按所述匹配度由高到低进行排序，找出排在前面的预设数量的社交网络用户为该用户的推荐好友；将所述推荐好友发送给所述用户。Preferably, the matching unit is specifically configured to compare the personal information of the user with other social network users, and obtain the matching degree between the personal information of other social network users and the user's personal information; Sort to the lowest, find out the preset number of social network users in the front as the recommended friends of the user; send the recommended friends to the user.
优选的，所述匹配单元，具体用于接收设定的好友匹配条件；比对该用户与其他社交网络用户的个人信息，得出所述好友匹配条件下其他社交网络用户与该用户的匹配度；按所述匹配度由高到低进行排序，找出排在前面的预设数量的社交网络用户为该用户的推荐好友；将所述推荐好友发送给所述用户。Preferably, the matching unit is specifically configured to receive the set friend matching conditions; compare the personal information of the user with other social network users, and obtain the matching degree between other social network users and the user under the friend matching conditions ; Sort according to the degree of matching from high to low, and find out the top preset number of social network users as recommended friends of the user; send the recommended friends to the user.
实施本发明实施例，具有如下有益效果：Implementing the embodiment of the present invention has the following beneficial effects:
本发明实施例通过采集用户的行为特征数据，发送所述行为特征数据到社交网络，分析所述行为特征数据得出该用户的个人信息，比对该用户与其他社交网络用户的个人信息，找出与该用户的个人信息匹配的社交网络用户。本发明的方案由于采集的用户行为特征数据类型来自于个人长期行为，一方面便于用户正确的认识自己，一方面也省去了用户手动更新个人信息的麻烦，更重要的是提高了社交网络中用户个人信息的真实性；当开启社交网络的好友推荐功能或者主动搜索朋友时，基于真实度较高的个人信息可以匹配出更相符的好友，匹配方式更准确，提高好友推荐的准确性。The embodiment of the present invention collects the user's behavior characteristic data, sends the behavior characteristic data to the social network, analyzes the behavior characteristic data to obtain the user's personal information, and compares the user's personal information with other social network users' personal information to find out Find social network users that match the user's personal information. Since the collected user behavior feature data type comes from personal long-term behavior, the scheme of the present invention facilitates users to know themselves correctly on the one hand, saves users from the trouble of manually updating personal information on the one hand, and more importantly, improves social networks. The authenticity of the user's personal information; when the friend recommendation function of the social network is turned on or the friend is actively searched for, based on the personal information with higher authenticity, more matching friends can be matched, the matching method is more accurate, and the accuracy of friend recommendation is improved.
附图说明Description of drawings
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述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 or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings described below are only For some embodiments of the present invention, those skilled in the art can also obtain other drawings based on these drawings without creative work.
图1是本发明第一实施例的社交网络中推荐好友的方法的流程示意图。FIG. 1 is a schematic flowchart of a method for recommending friends in a social network according to a first embodiment of the present invention.
图2是本发明第三实施例的社交网络中推荐好友的系统的结构示意图。Fig. 2 is a schematic structural diagram of a system for recommending friends in a social network according to a third embodiment of the present invention.
具体实施方式Detailed ways
下面结合本发明的附图对本发明实施例中的技术方案进行清楚、完整的描述，显然，所描述的实施例仅仅是本发明的一部分实施例，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作出创造性劳动的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 is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.
第一实施例first embodiment
图1是本发明第一实施例的社交网络中推荐好友的方法流程图，第一实施例详述如下：Fig. 1 is a flowchart of a method for recommending friends in a social network according to a first embodiment of the present invention, and the first embodiment is described in detail as follows:
步骤S101，采集用户的行为特征数据。Step S101, collect user behavior characteristic data.
在第一实施例中，所述的特征数据可以包括：用户出入的场所类别信息、运动信息和日常起居信息，其中，所述运动信息包括运动时间、运动强度及运动频率，所述日常起居信息包括心率及进食卡路里。In the first embodiment, the feature data may include: information on the type of places the user enters and exits, exercise information and daily life information, wherein the exercise information includes exercise time, exercise intensity and exercise frequency, and the daily life information Including heart rate and calories eaten.
本实施例中可通过智能穿戴手表、手环等设备实时采集用户的行为特征数据。这类智能可穿戴设备需满足硬件条件为：具备多种传感器，通过各传感器收集不同用户数据。例如具备处理器、定位模组、动作传感器、心率传感器、蓝牙等通信模组。所述定位模组，定位模组可以获取用户经常出入的场所类别，比如图书馆、球馆、公园、郊外、各种具体的培训班、健身俱乐部、商场等；动作传感器可以获取用户的运动记录，包括运动时间、运动强度与运动频率等健身信息；心率传感器可以获取的作息时间，包括早晨起床时间以及晚间的睡眠质量等信息；蓝牙等通信模组则用于上传采集到的行为特征数据。由于通过智能穿戴设备采集的用户行为特征数据类型来自于个人长期行为，一方面便于用户正确的认识自己，降低了用户填写个人信息的难度，同时也提高了社交网络中用户个人信息的真实性。In this embodiment, the user's behavior characteristic data can be collected in real time through devices such as smart wearable watches and bracelets. This type of smart wearable device needs to meet the hardware requirements: it has a variety of sensors and collects different user data through each sensor. For example, it has communication modules such as a processor, a positioning module, a motion sensor, a heart rate sensor, and Bluetooth. The positioning module, the positioning module can obtain the types of places that the user frequently enters and exits, such as libraries, arenas, parks, suburbs, various specific training courses, fitness clubs, shopping malls, etc.; the motion sensor can obtain the user's exercise records , including fitness information such as exercise time, exercise intensity, and exercise frequency; the work and rest time that can be obtained by the heart rate sensor, including information such as morning wake-up time and night sleep quality; Bluetooth and other communication modules are used to upload collected behavioral feature data. Since the type of user behavior characteristic data collected by smart wearable devices comes from personal long-term behavior, on the one hand, it is convenient for users to know themselves correctly, reducing the difficulty for users to fill in personal information, and at the same time improving the authenticity of users' personal information in social networks.
需要说明的是，根据实际情况可添加其他类型的传感器，例如可获取用户日常进食的卡路里情况的传感器，或者选择其他智能穿戴设备，如智能眼镜、智能戒指等，以实时采集用户的行为特征数据。It should be noted that other types of sensors can be added according to the actual situation, such as sensors that can obtain the calorie status of the user's daily eating, or choose other smart wearable devices, such as smart glasses, smart rings, etc., to collect user behavioral data in real time .
第一实施例中，可通过智能穿戴设备按照第一设定时间周期采集用户的行为特征数据。例如每半小时或一小时采集一次用户行为特征数据，采集的用户行为特征数据越多，越容易反映出用户的行为特征。In the first embodiment, the user's behavior characteristic data may be collected by the smart wearable device according to the first set time period. For example, user behavior characteristic data is collected every half hour or hour. The more user behavior characteristic data collected, the easier it is to reflect user behavior characteristics.
步骤S102，发送所述行为特征数据到社交网络。Step S102, sending the behavior characteristic data to social network.
第一实施例中，由于所述行为特征数据可能涉及到用户的个人私密数据，所以发送数据之前，需要对行为特征数据上传社交网络进行授权，只有通过发送授权验证时，才发送所述行为特征数据到社交网络，若否，拒绝上传所述行为特征数据。In the first embodiment, since the behavioral characteristic data may involve the user’s personal private data, before sending the data, it is necessary to authorize the uploading of the behavioral characteristic data to the social network. data to the social network, if not, refuse to upload the behavior characteristic data.
本实施例中，按照第二设定时间周期上传该用户的所述个人信息到社交网络，并且所述第二设定时间周期大于等于所述第一设定时间周期。例如每天发送一次所述行为特征数据，通过自动发送行为特征数据，可以随时调整用户当前的个人信息并及时更新到社交网络，即省去了用户手动输入个人信息的难度，又避免了用户修改个人信息的麻烦。In this embodiment, the personal information of the user is uploaded to the social network according to a second set time period, and the second set time period is greater than or equal to the first set time period. For example, the behavioral feature data is sent once a day. By automatically sending the behavioral feature data, the user’s current personal information can be adjusted at any time and updated to the social network in time, which saves the user from the difficulty of manually entering personal information and avoids the user from modifying personal information. Information trouble.
作为本实施例一优选实施方式，发送所述行为特征数据时，可统一上传所述行为特征数据到预设服务器，社交网络服务器可向所述预设服务器请求获取所述行为特征数据。当该预设服务器对应多个社交网络时，可节省智能穿戴设备发送数据的通信流量，节省网络资源。As a preferred implementation of this embodiment, when sending the behavior feature data, the behavior feature data may be uploaded to a preset server in a unified manner, and the social network server may request the preset server to obtain the behavior feature data. When the preset server corresponds to multiple social networks, it can save the communication traffic of the smart wearable device to send data, and save network resources.
步骤S103，分析所述行为特征数据得出该用户的个人信息。Step S103, analyzing the behavior characteristic data to obtain the user's personal information.
本实施例中，分析用户经常出入的场所类别，比如图书馆、球馆、公园、郊外、各种具体的培训班、健身俱乐部、商场等场所类别，可得出该用户是否爱阅读、经常去球场以及喜欢什么运动、特长培训班种类等，可以一定程度反映出用户的兴趣爱好类型，例如运动、娱乐、旅行、美食、阅读和/或购物等；通过分析用户的运动信息可得出该用户偏好的运动类型，例如晨练、晚练、有氧运动(瑜伽、太极等舒缓的运动)和/或体能运动(划船、冲浪、器械运动等比较剧烈的运动)；通过分析用户的日常起居信息得出该用户的日常作息规律，例如时早睡早起、经常熬夜等习惯；这些数据本身就可以在交友过程中受到关注。In this embodiment, by analyzing the types of places that users frequently visit, such as libraries, arenas, parks, suburbs, various specific training courses, fitness clubs, shopping malls and other places, it can be concluded whether the user likes to read, often goes to The stadium, what sports you like, the types of special training courses, etc., can reflect the user's hobbies to a certain extent, such as sports, entertainment, travel, food, reading and/or shopping, etc.; by analyzing the user's sports information, it can be concluded that the user Preferred type of exercise, such as morning exercise, evening exercise, aerobic exercise (yoga, Tai Chi, etc.) Find out the user's daily routine, such as going to bed early and getting up early, often staying up late and other habits; these data themselves can be paid attention to in the process of making friends.
优选的，本实施例中对通过分析得出的个人信息进行标记，标记这类个人信息的类型为真实数据，以区别与通过用户手动录入的个人信息，有利于社交网络信息的公开和透明管理。Preferably, in this embodiment, the personal information obtained through analysis is marked, and the type of such personal information is marked as real data, so as to distinguish it from the personal information manually entered by the user, which is conducive to the openness and transparent management of social network information .
通过综合分析用户的日常行为习惯得出的，可更真实的反应用户的行为特征和兴趣爱好，在一定程度上也便于用户更准确的把握自己的特点，另一方面也可避免用户手动输入个人信息的不便。Through the comprehensive analysis of the user's daily behavior habits, it can more truly reflect the user's behavior characteristics and hobbies, and to a certain extent, it is also convenient for the user to grasp their own characteristics more accurately. Information inconvenience.
步骤S104，比对该用户与其他社交网络用户的个人信息，找出与该用户的个人信息匹配的社交网络用户。Step S104, comparing the personal information of the user with other social network users, and finding a social network user matching the user's personal information.
在第一实施例中，当开启社交网络的好友推荐功能或者主动搜索朋友时，可通过比对该用户与其他社交网络用户的个人信息，得出其他社交网络用户的个人信息与该用户的个人信息的匹配度，以匹配出与自己更相符的好友推荐给所述用户，匹配方式更准确，提高好友推荐的准确性。In the first embodiment, when the friend recommendation function of the social network is turned on or actively searches for friends, the personal information of other social network users and the personal information of the user can be obtained by comparing the personal information of the user with other social network users. The matching degree of the information is used to match friends who are more consistent with oneself and recommend them to the user. The matching method is more accurate and the accuracy of friend recommendation is improved.
作为一优选实施方式，社交网络可以根据用户的个人信息自动为用户匹配推荐兴趣爱好相似或生活习惯相似的好友，社交网络后台通过比对该用户与其他社交网络用户的个人信息，得出其他社交网络用户的个人信息与该用户的个人信息的匹配度；然后再找出所述匹配度由高到低排序前设定数量的社交网络用户为该用户的推荐好友。例如按所述匹配度由高到低进行排序，找出排在前面10个的社交网络用户为该用户的推荐好友；将所述推荐好友发送给所述用户。As a preferred implementation, the social network can automatically match and recommend friends with similar hobbies or living habits for the user according to the user's personal information. The degree of matching between the personal information of the network user and the personal information of the user; and then find out the number of social network users who are set before sorting the degree of matching from high to low as the recommended friends of the user. For example, sort according to the matching degree from high to low, find out the top 10 social network users as the user's recommended friends; send the recommended friends to the user.
作为另一优选实施方式，用户可自行设定好友匹配的调节，例如按照兴趣爱好匹配，或在按照饮食习惯匹配，或在按照距离远近进行匹配等，接收到用户设定的好友匹配条件后，通过比对该用户与其他社交网络用户的个人信息，得出所述好友匹配条件下其他社交网络用户与该用户的匹配度；找出所述匹配度由高到低排序前设定数量的社交网络用户为该用户的推荐好友，例如按所述匹配度由高到低进行排序，找出排在前面10个的社交网络用户为该用户的推荐好友；将所述推荐好友发送给所述用户。用户还可以设定多个匹配调节的组合，例如搜索周边一定距离范围内具有相兴趣爱好的人，临时邀请一起运动或者活动，提供了一种更快捷更方便的社交组团方式。As another preferred implementation, the user can set the adjustment of friend matching by himself, such as matching according to hobbies, or matching according to eating habits, or matching according to distance, etc., after receiving the friend matching conditions set by the user, By comparing the personal information of the user with other social network users, the matching degree between other social network users and the user is obtained under the friend matching condition; find out the set number of social network users before sorting the matching degree from high to low The network user is the recommended friend of the user, such as sorting from high to low according to the matching degree, and finds out that the top 10 social network users are recommended friends of the user; the recommended friends are sent to the user . Users can also set a combination of multiple matching adjustments, such as searching for people with similar hobbies within a certain distance, and temporarily inviting them to exercise or do activities together, providing a faster and more convenient way to form social groups.
通过上述第一实施例，通过采集用户的行为特征数据，发送所述行为特征数据到社交网络，分析所述行为特征数据得出该用户的个人信息，一方面便于用户正确的认识自己，一方面也省去了用户手动输入个人信息的麻烦，更重要的是提高了社交网络中用户个人信息的真实性；当开启社交网络的好友推荐功能或者主动搜索朋友时，基于真实度较高的个人信息可以匹配出更相符的好友，匹配方式更准确，提高好友推荐的准确性。Through the above-mentioned first embodiment, by collecting the user’s behavior characteristic data, sending the behavior characteristic data to the social network, and analyzing the behavior characteristic data to obtain the user’s personal information, on the one hand, it is convenient for the user to know himself correctly, on the other hand It also saves users the trouble of manually inputting personal information, and more importantly, improves the authenticity of users' personal information in social networks; More matching friends can be matched, the matching method is more accurate, and the accuracy of friend recommendation can be improved.
第二实施例second embodiment
第二实施例提供的社交网络中推荐好友的系统的，与上述的方法实施例属于同一构思，系统的实施例中未详尽描述的细节内容，可以参考上述方法实施例。The system for recommending friends in the social network provided by the second embodiment belongs to the same idea as the above-mentioned method embodiment, and details not described in detail in the system embodiment can refer to the above-mentioned method embodiment.
图2示出了本发明第二实施例的社交网络中推荐好友的系统结构示意图，下面进行详细说明。Fig. 2 shows a schematic structural diagram of a system for recommending friends in a social network according to a second embodiment of the present invention, which will be described in detail below.
所述系统包括：智能穿戴设备10、与所述智能穿戴设备10通信的社交网络服务器20，所述智能穿戴设备10包括数据采集单元101和数据上传单元102，所述社交网络服务器20包括分析单元201和匹配单元202.各部分详述如下：The system includes: a smart wearable device 10, a social network server 20 communicating with the smart wearable device 10, the smart wearable device 10 includes a data collection unit 101 and a data upload unit 102, and the social network server 20 includes an analysis unit 201 and matching unit 202. Each part is described in detail as follows:
所述数据采集单元101，用于采集用户的行为特征数据。The data collection unit 101 is configured to collect user behavior characteristic data.
第二实施例中，所述特征数据包括：用户出入的场所类别信息、运动信息和日常起居信息等。其中，所述运动信息包括运动时间、运动强度及运动频率，所述日常起居信息包括心率等。对应的，所述采集单元101可以包括定位模组、动作传感器模组、心率传感器模组等。In the second embodiment, the feature data includes: information on the type of places the user enters and exits, sports information, daily life information, and the like. Wherein, the exercise information includes exercise time, exercise intensity and exercise frequency, and the daily life information includes heart rate and the like. Correspondingly, the acquisition unit 101 may include a positioning module, a motion sensor module, a heart rate sensor module, and the like.
优选的，本实施例中所述数据采集单元101按照第一设定时间周期采集用户的行为特征数据.例如每半小时或一小时采集一次用户行为特征数据，采集的用户行为特征数据越多，越容易反映出用户的行为特征。Preferably, the data collection unit 101 in this embodiment collects user behavior characteristic data according to a first set time period. For example, user behavior characteristic data is collected every half hour or hour, and the more user behavior characteristic data collected, The easier it is to reflect the user's behavioral characteristics.
所述数据上传单元102，用于发送所述行为特征数据到社交网络服务器20。The data uploading unit 102 is configured to send the behavior characteristic data to the social network server 20 .
第二实施例中，为了保证用户个人信息的私密性和安全性，发送用户行为特征数据之前需要经过授权验证，因此所述智能穿戴设备10还包括授权单元，用于发送所述行为特征数据到社交网络之前，对行为特征数据上传社交网络进行授权。若通过授权，允许上传所述行为特征数据，否则，禁止上传所述行为特征数据。In the second embodiment, in order to ensure the privacy and security of the user's personal information, authorization verification is required before sending the user behavior characteristic data, so the smart wearable device 10 also includes an authorization unit for sending the behavior characteristic data to Before the social network, authorize the uploading of behavior characteristic data to the social network. If the authorization is passed, the uploading of the behavior characteristic data is permitted, otherwise, the uploading of the behavior characteristic data is prohibited.
作为一优选实施方式，可上传所述行为特征数据到预设服务器，所述社交网络服务器20可向所述预设服务器请求获取所述行为特征数据。当该预设服务器对应多个社交网络服务器20时，该方式可以减小智能穿戴设备10的数据上传负荷，有利于节省网络流量。As a preferred implementation manner, the behavior characteristic data can be uploaded to a preset server, and the social network server 20 can request the behavior characteristic data from the preset server. When the preset server corresponds to multiple social network servers 20, this method can reduce the data upload load of the smart wearable device 10, which is beneficial to save network traffic.
优选的，本实施例中数据上传单元102按照第二设定时间周期上传该用户的所述个人信息到社交网络，并设定所述第二设定时间周期大于等于所述第一设定时间周期，例如每天发送一次所述行为特征数据。Preferably, in this embodiment, the data uploading unit 102 uploads the user's personal information to the social network according to the second set time period, and sets the second set time period to be greater than or equal to the first set time Period, for example, the behavior feature data is sent once a day.
所述分析单元201，用于分析所述行为特征数据得出该用户的个人信息。The analysis unit 201 is configured to analyze the behavior characteristic data to obtain the user's personal information.
第二实施例中，分析单元201可具体用于：分析所述场所类别信息得出该用户的兴趣爱好类型。例如：根据用户经常出入的场所类别，如图书馆、球馆、公园、郊外、各种具体的培训班、健身俱乐部、商场等场所类别，分析出该用户是否爱阅读、经常去球场、或旅行、美食、购物的等兴趣爱好。分析单元201还可具体用于分析所述运动信息得出该用户偏好的运动类型，例如喜欢晨练、或者晚上运动，或者喜欢有舒缓的氧运动还是剧烈的体能运动等；分析单元201还可通过分析所述日常起居信息得出该用户的日常作息规律，例如生活规律、早睡早起或者经常熬夜等。通过综合分析用户的日常行为习惯得出的，可更真实的反应用户的行为特征和兴趣爱好，在一定程度上也便于用户更准确的把握自己的特点，另一方面也可避免用户手动更新个人信息的不便。In the second embodiment, the analysis unit 201 may be specifically configured to: analyze the location category information to obtain the user's interest type. For example: According to the types of places that users frequently visit, such as libraries, arenas, parks, suburbs, various specific training courses, fitness clubs, shopping malls, etc., analyze whether the user likes to read, often goes to the stadium, or travels , food, shopping and other hobbies. The analysis unit 201 can also be specifically used to analyze the exercise information to obtain the type of exercise preferred by the user, for example, he likes morning exercise or evening exercise, or he likes slow aerobic exercise or strenuous physical exercise, etc.; the analysis unit 201 can also pass Analyze the daily life information to obtain the user's daily work and rest rules, such as life rules, going to bed early and getting up early, or often staying up late. Through the comprehensive analysis of the user's daily behavior and habits, it can more truly reflect the user's behavior characteristics and hobbies, and to a certain extent, it is also convenient for the user to grasp their own characteristics more accurately. On the other hand, it can also prevent the user from manually updating personal information. Information inconvenience.
优选的，本实施例中分析单元201还用于在分析所述行为特征数据得出该用户的个人信息之后，标记所述个人信息的类型为真实数据。以使得社交网络中的用户个人信息更加公开和透明，减少了网络欺诈行为的发生。Preferably, in this embodiment, the analysis unit 201 is further configured to mark the type of the personal information as real data after analyzing the behavior characteristic data to obtain the user's personal information. In order to make the user's personal information in the social network more open and transparent, and reduce the occurrence of network fraud.
所述匹配单元202，用于比对该用户与其他社交网络用户的个人信息，找出与该用户的个人信息匹配的社交网络用户。The matching unit 202 is configured to compare the personal information of the user with other social network users, and find a social network user matching the user's personal information.
第二实施例中，社交网络服务器可根据用户的个人信息定期自动为用户推荐好友，通过比对该用户与其他社交网络用户的个人信息，得出其他社交网络用户的个人信息与该用户的个人信息的匹配度；找出所述匹配度由高到低排序前设定数量的社交网络用户为该用户的推荐好友，例如找出匹配度由高到低排序前10的社交网络用户推荐给该用户。当然，用户也可自行设定好友匹配的调节，例如按照兴趣爱好匹配，或在按照饮食习惯匹配，或在按照距离远近进行匹配等，社交网络服务器接收到用户设定的好友匹配条件后，通过比对该用户与其他社交网络用户的个人信息，得出所述好友匹配条件下其他社交网络用户与该用户的匹配度；找出所述匹配度由高到低排序前设定数量的社交网络用户为该用户的推荐好友，例如找出匹配度由高到低排序前10的社交网络用户推荐给该用户。用户还可以设定多个匹配调节的组合，例如搜索周边一定距离范围内具有相兴趣爱好的人，临时邀请一起运动或者活动，提供了一种更快捷更方便的社交组团方式。In the second embodiment, the social network server can automatically recommend friends for the user on a regular basis according to the user's personal information, and by comparing the personal information of the user with other social network users, the personal information of other social network users and the user's personal information can be obtained. Matching degree of information; find out that the social network users whose matching degree is ranked from high to low are the recommended friends of the user, for example, find out that the top 10 social network users who are ranked from high to low are recommended to this user user. Of course, the user can also set the adjustment of friend matching by himself, such as matching according to hobbies, or matching according to eating habits, or matching according to distance, etc., after the social network server receives the friend matching conditions set by the user, through Compare the personal information of the user with other social network users to obtain the matching degree between other social network users and the user under the friend matching condition; find out the set number of social networks before sorting the matching degree from high to low The user is the recommended friend of the user, for example, find out the top 10 social network users ranked in order of matching degree from high to low and recommend to the user. Users can also set a combination of multiple matching adjustments, such as searching for people with similar hobbies within a certain distance, and temporarily inviting them to exercise or do activities together, providing a faster and more convenient way to form social groups.
上述第二实施例，基于采集用户的行为特征数据，发送所述行为特征数据到社交网络，分析所述行为特征数据得出该用户的个人信息，一方面便于用户正确的认识自己，同时也提高了社交网络中用户个人信息的真实性；当开启社交网络的好友推荐功能或者主动搜索朋友时，基于真实度较高的个人信息可以匹配出更相符的好友，匹配方式更准确，提高好友推荐的准确性。In the above-mentioned second embodiment, based on collecting the behavior characteristic data of the user, sending the behavior characteristic data to the social network, and analyzing the behavior characteristic data to obtain the user’s personal information, on the one hand, it is convenient for the user to know himself correctly, and at the same time, it also improves the user’s personal information. The authenticity of the user's personal information in the social network is ensured; when the friend recommendation function of the social network is turned on or the friend is actively searched for, based on the personal information with high authenticity, more matching friends can be matched, the matching method is more accurate, and the friend recommendation rate is improved. accuracy.
以上所揭露的仅为本发明较佳实施例而已，当然不能以此来限定本发明之权利要求范围，因此，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，仍属本发明所涵盖的范围。The above disclosures are only preferred embodiments of the present invention, and certainly cannot limit the scope of the claims of the present invention. Therefore, any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention, Still belong to the scope covered by the present invention.
| Application Number | Priority Date | Filing Date | Title |
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| CN201410714499.2ACN104462308A (en) | 2014-11-27 | 2014-11-27 | Method and system for recommending friends in social network |
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| CN201410714499.2ACN104462308A (en) | 2014-11-27 | 2014-11-27 | Method and system for recommending friends in social network |
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| CN104462308Atrue CN104462308A (en) | 2015-03-25 |
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| CN201410714499.2APendingCN104462308A (en) | 2014-11-27 | 2014-11-27 | Method and system for recommending friends in social network |
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