Effective browsing technique based on behavioral collaborative filtering on social streams

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
Pages (from-to)1702-1710
Number of pages9
JournalProcedia Computer Science
Issue numberC
Publication statusPublished - 2014
EventInternational Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2014 - Gdynia, Poland
Duration: Sep 15 2014Sep 17 2014

All Science Journal Classification (ASJC) codes

  • Computer Science(all)


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