Manipulation Robustness of Collaborative Filtering Systems

Preprint English OPEN
Yan, Xiang ; Van Roy, Benjamin (2009)
  • Subject: Computer Science - Information Theory | recommendation system, collaborative filtering, manipulation, information theory, statistics | Computer Science - Learning
    • jel: jel:C11

A collaborative filtering system recommends to users products that similar users like. Collaborative filtering systems influence purchase decisions, and hence have become targets of manipulation by unscrupulous vendors. We provide theoretical and empirical results demonstrating that while common nearest neighbor algorithms, which are widely used in commercial systems, can be highly susceptible to manipulation, two classes of collaborative filtering algorithms which we refer to as linear and asymptotically linear are relatively robust. These results provide guidance for the design of future collaborative filtering systems.
  • References (37)
    37 references, page 1 of 4

    [1] Gediminas Adomavicius and Alexander Tuzhilin. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6):734-749, 2005.

    [2] Marko Balabanovic and Yoav Shoham. Fab: Content-based, collaborative recommendation. Communications of the ACM, 40(3):66-72, 1997.

    [3] James Bennett. The cinematch system: operation, scale coverage, accuracy impact. http://blog. recommenders06.com/wp-content/uploads/2006/09/1jimbennett.wmv, 2006.

    [4] Rajat Bhattacharjee and Ashish Goel. Algorithms and incentives for robust ranking. In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms. SIAM, 2007.

    [5] John Breese, David Heckerman, and Carl Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence, 1998.

    [6] Robin Burke, Bamshad Mobasher, Roman Zabicki, and Runa Bhaumik. Limited knowledge shilling attacks in collaborative filtering systems. In Proceedings of the Third IJCAI Workshop in Intelligent Techniques for Personalization, 2005.

    [7] Peter Cheeseman and John Stutz. Bayesian classification (autoclass): Theory and results. Advances in Knowledge Discovery and Data Mining, pages 153-180, 1996.

    [8] Chrysanthos Dellarocas. Strategic manipulation of internet opinion forums: Implications for consumers and firms. Management Science, 52(10):1577-1593, 2006.

    [9] Arthur Dempster, Nan Laird, and Donald Rubin. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society, 39(1):1-38, 1977.

    [10] Pedro Domingos and Michael Pazzani. On the optimality of the simple bayesian classifier under zero-one loss. Machine Learning, 29(2):103-130, 1997.

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