Integrating Opinion Leader and User Preference for Recommendation

  • Dong Wu
  • Kai Yang
  • Tao Wang
  • Weiang Luo
  • Huaqing Min
  • Yi Cai
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9052)

Abstract

Collaborative filtering (CF) is one of the most well-known and commonly used technology for recommender systems. However, it suffers from inherent issues such as data sparsity. Many works have been done by used additional information such as user attributes, tags and social relationships to address these problems. We proposed an algorithm named OLrs (Opinion Leaders for Recommender System) based on the trust relationships. Specifically, the opinion leaders who have a strong influence for the active user and an accurate evaluation of the recommend item will be identified. The prediction for a given item is generated by ratings of these opinion leaders and the active user. Experimental results based on Epinions data set demonstrated that the prediction accuracy of our method outperforms other approach.

Keywords

Recommender systems Data sparsity Opinion leader Matrix factorization 
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Notes

Acknowledgments

The authors are grateful to the anonymous reviewers and the helpful suggestion given by the partners. The research was supported by the National Natural Science Foundation of China (no. 61300137),the Foundation for Distinguished Young Teachers in Higher Education of Guangdong(no.Yq2014117), the Technology Project of Zhanjiang (no. 2013B01148), the Natural Science Foundation of Lingnan Normal College (no.QL1307, no.QL1410).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Dong Wu
    • 1
  • Kai Yang
    • 2
  • Tao Wang
    • 2
  • Weiang Luo
    • 2
  • Huaqing Min
    • 2
  • Yi Cai
    • 2
  1. 1.School of Information Science and TechnologyLingnan Normal CollegeZhanjiangChina
  2. 2.School of Software EngineeringSouth China University of TechnologyGuangzhouChina

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