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    • 2. 发明申请
    • Clustering a User's Connections in a Social Networking System
    • 在社交网络系统中聚合用户连接
    • US20130013682A1
    • 2013-01-10
    • US13179547
    • 2011-07-10
    • Yun-Fang JuanMing Hua
    • Yun-Fang JuanMing Hua
    • G06F15/16
    • G06Q50/01
    • 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.
    • 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。
    • 3. 发明申请
    • Top Friend Prediction for Users in a Social Networking System
    • 社交网络系统中用户的热门朋友预测
    • US20120271722A1
    • 2012-10-25
    • US13093744
    • 2011-04-25
    • Yun-Fang JuanMing Hua
    • Yun-Fang JuanMing Hua
    • G06Q30/02G06F15/18
    • G06Q10/04
    • 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.
    • 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。
    • 4. 发明申请
    • Contextually Relevant Affinity Prediction in a Social Networking System
    • 社交网络系统中的相关亲和度预测
    • US20120166532A1
    • 2012-06-28
    • US12978265
    • 2010-12-23
    • Yun-Fang JuanMing Hua
    • Yun-Fang JuanMing Hua
    • G06F15/16
    • G06Q30/0224G06Q50/01
    • 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.
    • 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。