Abstract:
Social bookmarking websites allow users to store, organize, and search bookmarks of web pages. Users of these services can annotate their bookmarks by using informal tags and other metadata, such as titles, descriptions, etc. This paper focuses on the task of resource item recommendation for social bookmarking website users. We examine how we can incorporate the social tagging information into traditional collaborative filtering (CF) algorithm in order to recommend resource items that user may interest. This paper proposes an implicit trust relationship generation method to develop a trust-aware recommender system for social bookmarking website users based on users’ tagging behavior and rating behavior on resource items. Experimental results show that the proposed resource recommender system based on implicit trust estimating approach outperforms the traditional recommender approach.
