Securing recommender systems against shilling attacks using social-based clustering

Xiangliang Zhang, Tak Man Desmond Lee, Georgios Pitsilis

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


Recommender systems (RS) have been found supportive and practical in e-commerce and been established as useful aiding services. Despite their great adoption in the user communities, RS are still vulnerable to unscrupulous producers who try to promote their products by shilling the systems. With the advent of social networks new sources of information have been made available which can potentially render RS more resistant to attacks. In this paper we explore the information provided in the form of social links with clustering for diminishing the impact of attacks. We propose two algorithms, CluTr and WCluTr, to combine clustering with "trust" among users. We demonstrate that CluTr and WCluTr enhance the robustness of RS by experimentally evaluating them on data from a public consumer recommender system © 2013 Springer Science+Business Media New York & Science Press, China.
Original languageEnglish (US)
Pages (from-to)616-624
Number of pages9
JournalJournal of Computer Science and Technology
Issue number4
StatePublished - Jul 5 2013

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computational Theory and Mathematics
  • Theoretical Computer Science
  • Software
  • Computer Science Applications


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