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    Clustering a user's connections in a social networking system
    1.
    发明授权
    Clustering a user's connections in a social networking system 有权

    公开(公告)号：US09846916B2

    公开(公告)日：2017-12-19

    申请号：US13179547

    申请日：2011-07-10

    申请人： Yun-Fang Juan ,  Ming Hua

    发明人： Yun-Fang Juan ,  Ming Hua

    IPC分类号： G06F15/16 ,  G06Q50/00

    CPC分类号： G06Q50/01

    摘要： A user's connections in a social networking system are grouped into a number of clusters based on a measure of the connections' relationships, or affinity, to each other. The affinities among the connections are based on the connections' own relationships and indicate a likelihood that the connections are in the same social circles. The clusters are formed based on the affinities among the user's connections, where the clusters tend to have connections that have relatively high affinities with the other connections the same cluster as compared to the connections who are not in the same cluster. An iterative hierarchical clustering algorithm may be used to collapse the connections into clusters based on affinities between pairs of the connections.

    Clustering a User's Connections in a Social Networking System
    2.
    发明申请
    Clustering a User's Connections in a Social Networking System 有权
    在社交网络系统中聚合用户连接

    公开(公告)号：US20130013682A1

    公开(公告)日：2013-01-10

    申请号：US13179547

    申请日：2011-07-10

    申请人： Yun-Fang Juan ,  Ming Hua

    发明人： Yun-Fang Juan ,  Ming Hua

    IPC分类号： G06F15/16

    CPC分类号： G06Q50/01

    摘要： A user's connections in a social networking system are grouped into a number of clusters based on a measure of the connections' relationships, or affinity, to each other. The affinities among the connections are based on the connections' own relationships and indicate a likelihood that the connections are in the same social circles. The clusters are formed based on the affinities among the user's connections, where the clusters tend to have connections that have relatively high affinities with the other connections the same cluster as compared to the connections who are not in the same cluster. An iterative hierarchical clustering algorithm may be used to collapse the connections into clusters based on affinities between pairs of the connections.

    摘要翻译： 基于对彼此的连接关系或亲和度的度量，社交网络系统中的用户的连接被分组为多个聚类。 连接之间的亲和力基于连接自身的关系，并指出连接在同一个社交圈中的可能性。 基于用户连接之间的亲和度形成集群，其中集群倾向于具有与不在同一集群中的连接相比具有相同集群的其他连接具有相对高亲和度的连接。 可以使用迭代层次聚类算法基于连接对之间的亲和度将连接折叠成簇。

    Top Friend Prediction for Users in a Social Networking System
    3.
    发明申请
    Top Friend Prediction for Users in a Social Networking System 审中-公开
    社交网络系统中用户的热门朋友预测

    公开(公告)号：US20120271722A1

    公开(公告)日：2012-10-25

    申请号：US13093744

    申请日：2011-04-25

    申请人： Yun-Fang Juan ,  Ming Hua

    发明人： Yun-Fang Juan ,  Ming Hua

    IPC分类号： G06Q30/02 ,  G06F15/18

    CPC分类号： G06Q10/04

    摘要： A social networking system predicts a user's top friends among the user's connections in a social networking system. A top friend prediction model receives static data and statistics related to the historical interactions of the connection and the user as input singles. The model may be trained using a training set of data associated with the connections of users, where users have explicitly indicated that other users are or are not their top (or “best” or “closest”) friends. The trained model outputs a score for each of a particular user's connections, and the score is used to predict whether the connection is a top friend of that user. Whether a user's connection is one of that user's top friends thus indicates a closeness of that relationship in the real world, which may differ from how likely the users are to interact with each other within the social networking system.

    摘要翻译： 社交网络系统预测用户在社交网络系统中的用户连接中的最佳朋友。 顶级朋友预测模型接收与连接和用户的历史相互作用相关的静态数据和统计信息作为输入单个。 可以使用与用户的连接相关联的数据的训练集来训练该模型，其中用户明确地指出其他用户是或不是他们的顶部（或最佳或最接近）的朋友。 经过训练的模型为每个特定用户的连接输出分数，并且分数用于预测该连接是否是该用户的最佳朋友。 用户的连接是否是该用户的顶级朋友之一，因此表明该关系在现实世界中的接近度，这可能与用户在社交网络系统内彼此交互的可能性有差异。

    Contextually Relevant Affinity Prediction in a Social Networking System
    4.
    发明申请
    Contextually Relevant Affinity Prediction in a Social Networking System 审中-公开
    社交网络系统中的相关亲和度预测

    公开(公告)号：US20120166532A1

    公开(公告)日：2012-06-28

    申请号：US12978265

    申请日：2010-12-23

    申请人： Yun-Fang Juan ,  Ming Hua

    发明人： Yun-Fang Juan ,  Ming Hua

    IPC分类号： G06F15/16

    CPC分类号： G06Q30/0224 ,  G06Q50/01

    摘要： A tunable affinity function serves one or more processes running in a social networking environment, where each process may request a measure of affinity for a particular user. A module that implements the affinity function computes the requested measure of affinity by combining (e.g., adding) a weighted set of predictor functions, where each predictor function predicts whether the user will perform a different action. The weights are provided by the process that requests the measure of affinity, which allows the requesting process to weight the predictor functions differently and thus tune the affinity function for its own purpose.

    摘要翻译： 可调亲和度功能服务于在社交网络环境中运行的一个或多个进程，其中每个进程可以请求对特定用户的亲和度的度量。 实现亲和度功能的模块通过组合（例如，添加）预测器函数的加权集合来计算所请求的亲和度度量，其中每个预测器函数预测用户是否将执行不同的动作。 权重由请求亲和度测量的过程提供，这允许请求过程对预测器的功能进行不同的加权，并因此调整亲和力功能以达到其自身目的。

    TARGETING ADVERTISEMENTS IN A SOCIAL NETWORK
    8.
    发明申请
    TARGETING ADVERTISEMENTS IN A SOCIAL NETWORK 审中-公开
    在社会网络中指导广告

    公开(公告)号：US20090070219A1

    公开(公告)日：2009-03-12

    申请号：US12195321

    申请日：2008-08-20

    申请人： Adam D'Angelo ,  Aditya Agarwal ,  Kang-Xing Jin ,  Yun-Fang Juan ,  Levy Klots ,  Oleksandr Moskalyuk ,  Yishan Wong

    发明人： Adam D'Angelo ,  Aditya Agarwal ,  Kang-Xing Jin ,  Yun-Fang Juan ,  Levy Klots ,  Oleksandr Moskalyuk ,  Yishan Wong

    IPC分类号： G06Q30/00 ,  G06Q10/00 ,  G06Q40/00 ,  G06Q90/00

    CPC分类号： H04L67/306 ,  G06Q10/10 ,  G06Q30/02 ,  G06Q30/0247 ,  G06Q30/0251 ,  G06Q30/0258 ,  G06Q30/0269 ,  G06Q40/04 ,  H04L67/18 ,  H04L67/22 ,  H04W4/029

    摘要： A social networking website logs information about actions taken by members of the website. For a particular member of the website, the website presents targeted ads based on actions by the member and one or more characteristics of the member. The social networking website maintains a profile associated with the member which describes characteristics of the member, such as age, geographic location, employment, educational history and interests. The social networking website compares the member profile to targeting criteria for a plurality of advertising requests and determines the advertising requests that match the member profile and generate the most revenue for the social networking website. When presenting a member with an ad, the website may optimize advertising revenue by selecting an ad from the received ads that will maximize the expected value of the ad.

    摘要翻译： 社交网站记录有关网站成员采取的行动的信息。 对于网站的特定成员，网站根据会员的行为和会员的一个或多个特征提供有针对性的广告。 社交网站维护与成员有关的简介，描述会员的特征，如年龄，地理位置，就业，教育历史和兴趣。 社交网站将成员简档与多个广告请求的定位标准进行比较，并确定与成员简档匹配的广告请求，并为社交网站创造最多的收入。 在向成员展示广告时，网站可以通过从接收到的广告中选择一个可以最大化广告预期价值的广告来优化广告收入。