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

所属专题: Artificial Intelligence and Pattern Recognition

• Special Section on Selected Paper from NPC 2011 • 上一篇    下一篇


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

  • 收稿日期:2012-09-10 修回日期:2013-05-02 出版日期:2013-07-05 发布日期:2013-07-05

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

推荐系统在电子商务中扮演着非常关键的角色,不仅为商家带来了利益,而且为用户提供了合理选择商品的便利.随着推荐系统的广泛应用,不良商家也趁机利用推荐系统的推荐机制来推销自己的产品.托攻击就是其中一种简单有效但又难以检测的攻击.具体地,不良商家通过注册一些假用户作为“托”,假意与正常用户兴趣相投,使得推荐系统误认为“托”与正常用户的喜好一致.在此条件下,“托”极力表现出对某特殊产品的喜爱,推荐系统也会错误地将这些产品推荐给正常用户.正常用户在不知情的状况下购买了这些产品,托攻击的目的就成功达到了.本文研究了如何利用社交网络信息来防护推荐系统,使得托攻击不能得逞.本文提出的两种方法,CluTr 和 WCluTr,其主要思想是利用社交网络中的信任信息来加强用户群内部相同喜好的真实性.实验采用Epinions.com数据来验证所提方法的有效性.实验结果表明本文提出的方法可以大大降低托攻击蛊惑正常用户误选择的概率.

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.

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