Speak the same language with your friends: augmenting tag recommenders with social relations

Authors:
Kaipeng Liu;Binxing Fang;Weizhe Zhang
Affiliations:
Harbin Institute of Technology, Harbin, China;Harbin Institute of Technology, Harbin, China & Chinese Academic of Sciences, Beijing, China;Harbin Institute of Technology, Harbin, China
Venue:
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Year:
2010

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Abstract

Many existing tag recommendation approaches ignore the social relations between users. In this paper, we investigate the role of such additional information for the task of personalized tag recommendation. We inject the social relations between users and the content similarities between resources, along with the social annotations made by collaborative users, into a graph representation. To fully explore the structure of this graph, we exploit the methodology of random-walk computation of similarities between all the objects. We develop a personalized collaborative filtering algorithm that combines both the collaborative information and the personalized tag preferences. Experiments on Delicious data demonstrate the effectiveness of the proposed methods.