Tag Based Collaborative Filtering for Recommender Systems

Authors:
Huizhi Liang;Yue Xu;Yuefeng Li;Richi Nayak
Affiliations:
School of Information Technology, Queensland University of Technology, Brisbane, Australia;School of Information Technology, Queensland University of Technology, Brisbane, Australia;School of Information Technology, Queensland University of Technology, Brisbane, Australia;School of Information Technology, Queensland University of Technology, Brisbane, Australia
Venue:
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Year:
2009

Citing 4
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Abstract

Collaborative tagging can help users organize, share and retrieve information in an easy and quick way. For the collaborative tagging information implies user's important personal preference information, it can be used to recommend personalized items to users. This paper proposes a novel tag-based collaborative filtering approach for recommending personalized items to users of online communities that are equipped with tagging facilities. Based on the distinctive three dimensional relationships among users, tags and items, a new similarity measure method is proposed to generate the neighborhood of users with similar tagging behavior instead of similar implicit ratings. The promising experiment result shows that by using the tagging information the proposed approach outperforms the standard user and item based collaborative filtering approaches.