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Journal of Computer Science and Technology

, Volume 28, Issue 4, pp 616–624 | Cite as

Securing Recommender Systems Against Shilling Attacks Using Social-Based Clustering

  • Xiang-Liang Zhang
  • Tak Man Desmond Lee
  • Georgios Pitsilis
Regular Paper

Abstract

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 Epinions.com.

Keywords

shilling attack recommender system collaborative filtering social trust clustering 

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Copyright information

© Springer Science+Business Media New York & Science Press, China 2013

Authors and Affiliations

  • Xiang-Liang Zhang
    • 1
  • Tak Man Desmond Lee
    • 1
  • Georgios Pitsilis
    • 2
  1. 1.King Abdullah University of Science and TechnologyThuwalSaudi Arabia
  2. 2.Faculty of Science, Technology and CommunicationUniversity of LuxembourgLuxembourgLuxembourg

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