Exploiting users’ rating behaviour to enhance the robustness of social recommendation

Zizhu Zhang*, Weiliang Zhao, Jian Yang, Surya Nepal, Cecile Paris, Bing Li

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationWeb Information Systems Engineering – WISE 2017
Subtitle of host publication18th International Conference, Puschino, Russia, October 7–11, 2017. Proceedings, Part II
EditorsAthman Bouguettaya, Yunjun Gao, Andrey Klimenko, Lu Chen, Xiangliang Zhang, Fedor Dzerzhinskiy, Weijia Jia, Stanislav V. Klimenko, Qing Li
Place of PublicationCham
PublisherSpringer, Springer Nature
Pages467-475
Number of pages9
ISBN (Electronic)9783319687865
ISBN (Print)9783319687858
DOIs
Publication statusPublished - 2017
Event18th International Conference on Web Information Systems Engineering, WISE 2017 - Puschino, Russian Federation
Duration: 7 Oct 201711 Oct 2017

Publication series

NameLecture Notes in Computer Science
Volume10570
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Web Information Systems Engineering, WISE 2017
CountryRussian Federation
CityPuschino
Period7/10/1711/10/17

Keywords

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

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