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Opinion-Based Collaborative Filtering to Solve Popularity Bias in Recommender Systems

  • Xiangyu Zhao
  • Zhendong Niu
  • Wei Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8056)

Abstract

Existing recommender systems suffer from a popularity bias problem. Popular items are always recommended to users regardless whether they are related to users’ preferences. In this paper, we propose an opinion-based collaborative filtering by introducing weighting functions to adjust the influence of popular items. Based on conventional user-based collaborative filtering, the weighting functions are used in measuring users’ similarities so that the effect of popular items is decreased with similar opinions and increased with dissimilar ones. Experiments verify the effectiveness of our proposed approach.

Keywords

recommender system popularity bias collaborative filtering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Xiangyu Zhao
    • 1
  • Zhendong Niu
    • 1
  • Wei Chen
    • 2
    • 1
  1. 1.School of Computer Science and TechnologyBeijing Institute of TechnologyBeijingChina
  2. 2.Agricultural Information InstituteChinese Academy of Agricultural SciencesBeijingChina

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