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Strategies for Effective Shilling Attacks against Recommender Systems

  • Sanjog Ray
  • Ambuj Mahanti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5456)

Abstract

One area of research which has recently gained importance is the security of recommender systems. Malicious users may influence the recommender system by inserting biased data into the system. Such attacks may lead to erosion of user trust in the objectivity and accuracy of the system. In this paper, we propose a new approach for creating attack strategies. Our paper explores the importance of target item and filler items in mounting effective shilling attacks. Unlike previous approaches, we propose strategies built specifically for user based and item based collaborative filtering systems. Our attack strategies are based on intelligent selection of filler items. Filler items are selected on the basis of the target item rating distribution. We show through experiments that our strategies are effective against both user based and item based collaborative filtering systems. Our approach is shown to provide substantial improvement in attack effectiveness over existing attack models.

Keywords

Recommender systems Shilling attacks Collaborative filtering 

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References

  1. 1.
    Burke, R., Mobasher, B., Bhaumik, R.: Limited Knowledge Shilling Attacks in Collaborative Filtering Systems. In: Proceedings of Workshop on Intelligent Techniques for Web Personalization, Edinburgh (2005)Google Scholar
  2. 2.
    Herlocker, J., Konstan, J., Borchers, A., Riedl, J.: An Algorithm Framework for Performing Collaborative Filtering. In: Proceedings of SIGIR, pp. 77–87. ACM, New York (1999)Google Scholar
  3. 3.
    Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., Riedl, J.: GroupLens: Applying Collaborative Filtering to Usenet News. Communications of the ACM 40(3), 77–87 (1997)CrossRefGoogle Scholar
  4. 4.
    Lam, S., Riedl, J.: Shilling Recommender Systems for Fun and Profit. In: Proceedings of the 13th International WWW Conference, New York (2004)Google Scholar
  5. 5.
    Leino, J., Raiha, K.: Case Amazon: Ratings and Reviews as Part of Recommendations. In: Proceedings of the 2007 ACM Conference on Recommender Systems, Minneapolis, pp. 137–140 (2007)Google Scholar
  6. 6.
    Mehta, B., Hofmann, T., Nejdl, W.: Robust Collaborative Filtering. In: Proceedings of the 2007 ACM Conference on Recommender Systems, Minneapolis, pp. 49–56 (2007)Google Scholar
  7. 7.
    Mobasher, B., Burke, R., Bhaumik, R., Williams, C.: Effective Attack Models for Shilling Item-Based Collaborative Filtering Systems. In: Proceedings of the 2005 WebKDD Workshop, Chicago (2005)Google Scholar
  8. 8.
    Mobasher, B., Burke, R., Bhaumik, R., Williams, C.: Towards Trustworthy Recommender Systems: An Analysis of Attack Models and Algorithm Robustness. ACM Transactions on Internet Technology 7, 23, 1–38 (2007)Google Scholar
  9. 9.
    MovieLens data set, http://www.grouplens.org
  10. 10.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based Collaborative Filtering of Recommendation Algorithms. In: Proceedings of the 10th International WWW Conference, Hong Kong (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sanjog Ray
    • 1
  • Ambuj Mahanti
    • 1
  1. 1.Management Information Systems Group, JokaIndian Institute of Management CalcuttaKolkataIndia

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