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Personalized Recommendation by Exploring Social Users’ Behaviors

  • Guoshuai Zhao
  • Xueming Qian
  • He Feng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8326)

Abstract

With the popularity and rapid development of social network, more and more people enjoy sharing their experiences, such as reviews, ratings and moods. And there are great opportunities to solve the cold start and sparse data problem with the new factors of social network like interpersonal influence and interest based on circles of friends. Some algorithm models and social factors have been proposed in this domain, but have not been fully considered. In this paper, two social factors: interpersonal rating behaviors similarity and interpersonal interest similarity, are fused into a consolidated personalized recommendation model based on probabilistic matrix factorization. And the two factors can enhance the inner link between features in the latent space. We implement a series of experiments on Yelp dataset. And experimental results show the outperformance of proposed approach.

Keywords

recommender system entropy rating behaviors social networks 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Guoshuai Zhao
    • 1
  • Xueming Qian
    • 1
  • He Feng
    • 1
  1. 1.SMILES LABXi’an Jiaotong UniversityChina

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