Tag-aware recommender systems

Zhang, Zi-Ke; Zhou, Tao; Zhang, Yi-Cheng

doi:10.1007/s11390-011-0176-1

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Journal article

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

Zhang, Zi-Ke Institute of Information Economy, Hangzhou Normal University, China - Web Sciences Center, University of Electronic Science and Technology, Chengdu, China - Department of Physics, University of Fribourg, Switzerland
Zhou, Tao Web Sciences Center, University of Electronic Science and Technology, Chengdu, China
Zhang, Yi-Cheng Institute of Information Economy, Hangzhou Normal University, China - Web Sciences Center, University of Electronic Science and Technology, Chengdu, China - Department of Physics, University of Fribourg, Switzerland

23.09.2011

Published in:

Journal of Computer Science and Technology. - 2011, vol. 26, no. 5, p. 767-777

network-based/tensor-based/topic-based methods

English 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.

Faculty

Faculté des sciences et de médecine

Department

Département de Physique

Language