Topic-based web page recommendation using tags

Abstract

Collaborative tagging sites allow users to save and annotate their favorite web contents with tags. These tags provide a novel source of information for collaborative filtering. This paper proposes a probabilistic approach to leverage information embedded in tags to improve the effectiveness of Web page recommendation in a social information management context. In our approach, the probability of a Web page visit by a user is estimated by summing up the relevance of this Web page to this user's tags, and then those pages with the highest probabilities are recommended. Experiments using two real-world collaborative tagging datasets show that our algorithms outperform the common collaborative filtering methods.