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
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
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/302200
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