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Journal article

Website-oriented recommendation based on heat spreading and tag-aware collaborative filtering

  • Zhang, Zi-Ke Institute of Information Economy, Hangzhou Normal University, China - Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, China
  • Yu, Lu Institute of Information Economy, Hangzhou Normal University, China - Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, China
  • Fang, Kuan Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, China
  • You, Zhi-Qiang Institute of Information Economy, Hangzhou Normal University, China - Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, China
  • Liu, Chuang Institute of Information Economy, Hangzhou Normal University, China - Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, China
  • Liu, Hao Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, China - Department of Physics, University of Fribourg, Switzerland
  • Yan, Xiao-Yong School of Systems Science, Beijing Normal University, China
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    01.04.2014
Published in:
  • Physica A: Statistical Mechanics and its Applications. - 2014, vol. 399, p. 82–88
Recommender systems
Collaborative filtering
Heat spreading
SVD
Tag aware
English Recently, Recommender Systems has been widely applied in helping users find potentially interesting items from the era of big data. However, most of researches on this topic have focused on estimating the direct relationships between users and items, neglecting other available information. In this paper, we discuss about mining webs with information extracted from search and browser logs of users. In particular, we utilize the keywords correlated with corresponding websites by Singular Value Decomposition (SVD) technique to model users features and propose the tag-aware kk-nearest neighbor Collaborative Filtering (CF). We then build a hybrid recommendation method to help people accurately find websites by employing Heat Spreading (HeatS) method. Experimental results demonstrate that the hybrid method outperforms baseline algorithms at the global ranking metric.
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/303709
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