›› 2011, Vol. 26 ›› Issue (5): 767-777.doi: 10.1007/s11390-011-0176-1

Special Issue: Surveys

• Special Section on Community Analysis and Information Recommendation • Previous Articles     Next Articles

Tag-Aware Recommender Systems: A State-of-the-Art Survey

Zi-Ke Zhang1,2,3 (张子柯), Tao Zhou2 (周涛), and Yi-Cheng Zhang1,2,3 (张翼成)   

  1. 1. Institute of Information Economy, Hangzhou Normal University, Hangzhou 310036, China
    2. Web Sciences Center, University of Electronic Science and Technology, Chengdu 610054, China
    3. Department of Physics, University of Fribourg, Chemin du Musée 1700 Fribourg, Switzerland
  • Received:2011-01-18 Revised:2011-07-07 Online:2011-09-05 Published:2011-09-05
  • Contact: Zi-Ke Zhang E-mail:zhangzike@gmail.com, zhutouster@gmail.com; yi-cheng.zhang@unifr.ch
  • About author:Zi-Ke Zhang is a Ph.D. candidate of theoretical physics in University of Fribourg, Switzerland. His research interests include social tagging systems, recommender systems, complex networks, etc. He has published about 20 papers in the following journals: PLoS ONE, Physical Reviews, EPL, JSM and EPJB.
    Tao Zhou obtained his Ph.D. in theoretical physics from University of Fribourg, Switzerland. He serves as a professor in the Web Sciences Center. His main research interests include complex networks, information physics, human dynamics, collective dynamics, and so on. He has published about 60 papers in the following five journals: PLoS ONE, Physical Reviews, EPL, NJP and PNAS. Till July 2011, all his publications get more than 1800 citations from the Web of Science, and more than 3500 citations from Google Scholar. His H-index according to SCI citation is 22.
    Yi-Cheng Zhang is a professor of theoretical physics in University of Fribourg, Switzerland. He is the director of Institute of Information Economy in Hangzhou Normal University. His main research interests include complex systems, information physics, information economy, and so on. He has published about 130 papers in the following journals: Physics Report, PNAS, PLoS ONE, PRL, EPL, and NJP.
  • Supported by:

    This work is partially supported by the Future and Emerging Technologies (FET) Programs of the European Commission FP7-COSI-ICT (QLectives with Grant No. 231200 and LiquidPub with Grant No. 213360). Z.-K.Zhang and T.Zhou acknowledge the National Natural Science Foundation of China under Grant Nos. 11105024, 60973069, 61103109, and 90924011, and the Science and Technology Department of Sichuan Province under Grant No. 2010HH0002.

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.

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