Effective Browsing Technique based on Behavioral Collaborative Filtering on Social Streams
Effective Browsing Technique based on Behavioral Collaborative Filtering on Social Streams
复制标题
基于社交流行为协同过滤的有效浏览技术
DOI：
10.1016/j.procs.2014.08.263
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Taketoshi Ushiama
中科院分区：
文献类型：
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作者：
Hong Yan;Taketoshi Ushiama
近年来，社交网络服务（SNS）越来越受欢迎，并随时产生新的文章。然而，当大量的文章流从SNS中传递时，由于用户有时会跳过有价值的文章，因此不容易有效地浏览它们。在本文中，我们提出了一种方法来推荐一个未读的文章，以实现高效的浏览。我们的方法估计用户的浏览行为的基础上交付的文章的用户的偏好，并预测每个未读的文章的基础上的协同过滤方法的偏好。我们的系统基于可能与目标用户的阅读文章的行为高度相似的用户的行为来估计每个未读文章对于目标用户的价值，并且利用估计结果以适当的顺序将未读文章组成流，以实现高效的浏览。
In recent years, Social Networking Services (SNSs) are growing in popularity, and generating new articles moment by moment. However, when huge article streams are delivered from the SNS, it is not easy to browse them efficiently because users would sometimes skip valuable articles. In this paper, we propose a method to recommend an unread article in order to achieve efficient browsing. Our method estimates the preference of a user on a delivered article based on the browsing behavior of the user, and predicts the preference of each unread article based on the collaborative filtering approach. Our system estimates the value of each unread article for the target user based on the behaviors of users who might be highly similar to the target user's behavior of reading articles, and utilizes the estimation results for composing unread articles into a stream in an appropriate order to realize efficient browsing.