Lasso-based tag expansion and tag-boosted collaborative filtering

Authors:
Jian Shao;Lu Yao;Ruiyu Cai;Yin Zhang
Affiliations:
College of Computer Science and Technology, Zhejiang University, Hangzhou, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, China
Venue:
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Year:
2010

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Abstract

With the increasing popularity of the social tagging systems, tags can be effectively utilized to enhance Collaborative Filtering (CF) algorithms. Tags not only reflect users' preference, but also are a cue to describe the semantics of items. This paper formulates the problem of collaborative filtering as random walks over the user-item-tag tripartite graph. In order to alleviate the sparsity of tags, a lasso logistic regression model is conducted to accomplish tag expansion, i.e., adding relevant tags and removing irrelevant tags for each item. Experimental results on MovieLens dataset demonstrate the superiority of the proposed algorithms over several existing CF algorithms in terms of ranking performance measure F1 and Macro DOA.