Exploiting Users’ Rating Behaviour to Enhance the Robustness of Social Recommendation

  • Zizhu ZhangEmail author
  • Weiliang Zhao
  • Jian Yang
  • Surya Nepal
  • Cecile Paris
  • Bing Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10570)


In the rating systems, quite often it can be observed that some users rate few items, whereas some users rate a large number of items. Users’ rating scores also vary, i.e., some users’ scores are widely distributed while others are falling in a small range. Existing social recommendation approaches largely ignore such differences. We propose a peer-based relay recommendation method that exploits the credibility of users’ ratings. The credibility of a user’s rating is calculated according to its rating behaviour in terms of the number of ratings provided and the deviation from the normal behaviour. The credibility value of a user’s rating is incorporated when aggregating ratings from different users. Experiments are conducted on a large-scale social rating network for movie recommendations. The results show that the incorporation of credibility of users’ ratings can effectively reduce the impact of recommended rating noises with low credibility and enhance robustness of the system.


Social recommender system Rating behaviour Credibility Recommendation relay scheme Peer-based recommendation 



The work is supported by Australian Research Council Discovery Project, Australian Government RTP Scholarship, and CSIRO Top-up Scholarship.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Zizhu Zhang
    • 1
    Email author
  • Weiliang Zhao
    • 1
  • Jian Yang
    • 1
  • Surya Nepal
    • 2
  • Cecile Paris
    • 2
  • Bing Li
    • 3
  1. 1.Macquarie UniversitySydneyAustralia
  2. 2.CSIROSydneyAustralia
  3. 3.University of International Business and EconomicsBeijingChina

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