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Tag based collaborative filtering for recommender systems

Liang, Huizhi, Xu, Yue, Li, Yuefeng, & Nayak, Richi (2009) Tag based collaborative filtering for recommender systems. In Proceedings of Rough Sets and Knowledge Technology : 4th International Conference, Springer, WaterMark Hotel & Spa, Gold Coasts, Queensland, pp. 666-673.

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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.

Impact and interest:

1 citations in Scopus
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5 citations in Web of Science®

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262 since deposited on 17 Jan 2010
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ID Code: 29730
Item Type: Conference Paper
Additional URLs:
DOI: 10.1007/978-3-642-02962-2_84
ISBN: 9783642029615
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > School of Information Technology
Copyright Owner: Copyright 2009 Springer
Copyright Statement: This is the author-version of the work. Conference proceedings published, by Springer Verlag, will be available via Lecture Notes in Computer Science http://www.springer.de/comp/lncs/
Deposited On: 18 Jan 2010 07:40
Last Modified: 18 Jul 2014 11:28

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