›› 2013, Vol. 28 ›› Issue (4): 616-624.doi: 10.1007/s11390-013-1362-0

Special Issue: Artificial Intelligence and Pattern Recognition

• Special Section of EDB2012 • Previous Articles     Next Articles

Securing Recommender Systems Against Shilling Attacks Using Social-Based Clustering

Xiang-Liang Zhang1 (张响亮), Tak Man Desmond Lee1, and Georgios Pitsilis2   

  1. 1. King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia;
    2. Faculty of Science, Technology and Communication, University of Luxembourg, Luxembourg
  • Received:2012-09-10 Revised:2013-05-02 Online:2013-07-05 Published:2013-07-05

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.

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