Coupling Multiple Views of Relations for Recommendation

  • Bin Fu
  • Guandong XuEmail author
  • Longbing Cao
  • Zhihai Wang
  • Zhiang Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9078)


Learning user/item relation is a key issue in recommender system, and existing methods mostly measure the user/item relation from one particular aspect, e.g., historical ratings, etc. However, the relations between users/items could be influenced by multifaceted factors, so any single type of measure could get only a partial view of them. Thus it is more advisable to integrate measures from different aspects to estimate the underlying user/item relation. Furthermore, the estimation of underlying user/item relation should be optimal for current task. To this end, we propose a novel model to couple multiple relations measured on different aspects, and determine the optimal user/item relations via learning the optimal way of integrating these relation measures. Specifically, matrix factorization model is extended in this paper by considering the relations between latent factors of different users/items. Experiments are conducted and our method shows good performance and outperforms other baseline methods.


Recommender system Collaborative filtering Matrix factorization 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Bin Fu
    • 1
    • 2
  • Guandong Xu
    • 1
    Email author
  • Longbing Cao
    • 1
  • Zhihai Wang
    • 2
  • Zhiang Wu
    • 3
  1. 1.Advanced Analytics InstituteUniversity of TechnologySydneyAustralia
  2. 2.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina
  3. 3.Jiangsu Provincial Key Laboratory of E-BusinessNanjing University of Finance and EconomicsNanjingChina

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