Tag-aware recommender systems: a state-of-the-art survey

Authors:
Zi-Ke Zhang;Tao Zhou;Yi-Cheng Zhang
Affiliations:
Institute of Information Economy, Hangzhou Normal University, Hangzhou, China and Web Sciences Center, University of Electronic Science and Technology, Chengdu, China and Department of Physics, Un ...;Web Sciences Center, University of Electronic Science and Technology, Chengdu, China;Institute of Information Economy, Hangzhou Normal University, Hangzhou, China and Web Sciences Center, University of Electronic Science and Technology, Chengdu, China and Department of Physics, Un ...
Venue:
Journal of Computer Science and Technology - Special issue on Community Analysis and Information Recommendation
Year:
2011

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Abstract

In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related studies and future challenges of tag-aware recommendation algorithms.