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Exploring Social Relations for Personalized Tag Recommendation in Social Tagging Systems

Kaipeng LIU, Binxing FANG, Weizhe ZHANG

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Summary :

With the emergence of Web 2.0, social tagging systems become highly popular in recent years and thus form the so-called folksonomies. Personalized tag recommendation in social tagging systems is to provide a user with a ranked list of tags for a specific resource that best serves the user's needs. Many existing tag recommendation approaches assume that users are independent and identically distributed. This assumption ignores the social relations between users, which are increasingly popular nowadays. In this paper, we investigate the role of social relations in the task of tag recommendation and propose a personalized collaborative filtering algorithm. In addition to the social annotations made by collaborative users, we inject the social relations between users and the content similarities between resources into a graph representation of folksonomies. To fully explore the structure of this graph, instead of computing similarities between objects using feature vectors, we exploit the method of random-walk computation of similarities, which furthermore enable us to model a user's tag preferences with the similarities between the user and all the tags. We combine both the collaborative information and the tag preferences to recommend personalized tags to users. We conduct experiments on a dataset collected from a real-world system. The results of comparative experiments show that the proposed algorithm outperforms state-of-the-art tag recommendation algorithms in terms of prediction quality measured by precision, recall and NDCG.

Publication
IEICE TRANSACTIONS on Information Vol.E94-D No.3 pp.542-551
Publication Date
2011/03/01
Publicized
Online ISSN
1745-1361
DOI
10.1587/transinf.E94.D.542
Type of Manuscript
Special Section PAPER (Special Section on Data Engineering)
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