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Attack-Resistant Recommender Systems

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Abstract

The input to recommender systems is typically provided through open platforms. Almost anyone can register and submit a review at sites such as Amazon.com and Epinions.com. Like any other data-mining system, the effectiveness of a recommender system depends almost exclusively on the quality of the data available to it. Unfortunately, there are significant motivations for participants to submit incorrect feedback about items for personal gain or for malicious reasons:

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Notes

  1. 1.

    The ROC curve is used in a different context here than in Chapter 7 In Chapter 7, the ROC curve measures the effectiveness of ranking items for recommendations. Here, we measure the effectiveness of ranking user profiles based on their likelihood of being fake. However, the general principle of using the ROC curve is similar in both cases, because a ranking is compared with the binary ground-truth in both cases.

Bibliography

  1. G. K. Al Mamunur Rashid, G. Karypis, and J. Riedl. Influence in ratings-based recommender systems: An algorithm-independent approach. SIAM Conference on Data Mining, 2005.

    Google Scholar 

  2. X. Amatriain, J. Pujol, N. Tintarev, and N. Oliver. Rate it again: increasing recommendation accuracy by user re-rating. ACM Conference on Recommender Systems, pp. 173–180, 2009.

    Google Scholar 

  3. R. Bhaumik, C. Williams, B. Mobasher, and R. Burke. Securing collaborative filtering against malicious attacks through anomaly detection. Workshop on Intelligent Techniques for Web Personalization (ITWP), 2006.

    Google Scholar 

  4. R. Bhaumik, R. Burke, snd B. Mobasher. Crawling Attacks Against Web-based Recommender Systems. International Conference on Data Mining (DMIN), pp. 183–189, 2007.

    Google Scholar 

  5. K. Bryan, M. O’Mahony, and P. Cunningham. Unsupervised retrieval of attack profiles in collaborative recommender systems. ACM Conference on Recommender Systems, pp. 155–162, 2008.

    Google Scholar 

  6. R. Burke, M. O’Mahony, and N. Hurley. Robust collaborative recommendation. Recommender Systems Handbook, Springer, pp. 805–835, 2011.

    Google Scholar 

  7. R. Burke, B. Mobasher, R. Zabicki, and R. Bhaumik. Identifying attack models for secure recommendation. Beyond Personalization: A Workshop on the Next Generation of Recommender Systems, 2005.

    Google Scholar 

  8. R. Burke, B. Mobasher, and R. Bhaumik. Limited knowledge shilling attacks in collaborative filtering systems. IJCAI Workshop in Intelligent Techniques for Personalization, 2005.

    Google Scholar 

  9. R. Burke, B. Mobasher, C. Williams, and R. Bhaumik. Classification features for attack detection in collaborative recommender systems. ACM KDD Conference, pp. 542–547, 2006.

    Google Scholar 

  10. E. Candes, X. Li, Y. Ma, and J. Wright. Robust principal component analysis?. Journal of the ACM (JACM), 58(3), 11, 2011.

    Google Scholar 

  11. P. Chirita, W. Nejdl, and C. Zamfir. Preventing shilling attacks in online recommender systems. ACM International Workshop on Web Information and Data Management, pp. 67–74, 2005.

    Google Scholar 

  12. I. Gunes, C. Kaleli, A. Bilge, and H. Polat. Shilling attacks against recommender systems: a comprehensive survey. Artificial Intelligence Review, 42(4), 767–799, 2014.

    Article  Google Scholar 

  13. J. Herlocker, J. Konstan, L. Terveen, and J. Riedl. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS), 22(1), pp. 5–53, 2004.

    Article  Google Scholar 

  14. I. Jolliffe. Principal component analysis, 2nd edition, Springer, 2002.

    Google Scholar 

  15. S. Lam and J. Riedl. Shilling recommender systems for fun and profit. World Wide Web Conference, pp. 393–402, 2004.

    Google Scholar 

  16. M. O’Mahony, N. Hurley, and G. Silvestre. Promoting recommendations: An attack on collaborative filtering. Database and Expert Systems Applications, pp. 494–503, 2002.

    Google Scholar 

  17. M. O’Mahony, N. Hurley, G. Silvestre. An evaluation of the performance of collaborative filtering. International Conference on Artificial Intelligence and Cognitive Science (AICS), pp. 164–168, 2003.

    Google Scholar 

  18. M. O’Mahony, N. Hurley, G. Silvestre. Recommender systems: Attack types and strategies. National Conference on Artificial Intelligence (AAAI), pp. 334–339, 2005.

    Google Scholar 

  19. M. O’Mahony, N. Hurley, G. Silvestre. An evaluation of neighbourhood formation on the performance of collaborative filtering. Artificial Intelligence Review, 21(1), pp. 215–228, 2004.

    Article  MATH  Google Scholar 

  20. B. Mehta, and T. Hofmann. A survey of attack-resistant collaborative filtering algorithms. IEEE Data Enginerring Bulletin, 31(2), pp. 14–22, 2008.

    Google Scholar 

  21. B. Mehta, T. Hofmann, and P. Fankhauser. Lies and propaganda: detecting spam users in collaborative filtering. International Conference on Intelligent User Interfaces, pp. 14–21, 2007.

    Google Scholar 

  22. B. Mehta, T. Hofmann, and W. Nejdl. Robust collaborative filtering. ACM Conference on Recommender Systems, pp. 49–56, 2007.

    Google Scholar 

  23. B. Mehta and W. Nejdl. Unsupervised strategies for shilling detection and robust collaborative filtering. User Modeling and User-Adapted Interaction, 19(1–2), pp. 65–97, 2009.

    Article  Google Scholar 

  24. B. Mehta and W. Nejdl. Attack resistant collaborative filtering. ACM SIGIR Conference, pp. 75–82, 2008.

    Google Scholar 

  25. B. Mobasher, R. Burke, R. Bhaumik, and C. Williams. Toward trustworthy recommender systems: an analysis of attack models and algorithm robustness. ACM Transactions on Internet Technology (TOIT), 7(4), 23, 2007.

    Google Scholar 

  26. B. Mobasher, R. Burke, R. Bhaumik, and C. Williams. Effective attack models for shilling item-based collaborative filtering systems. WebKDD Workshop, 2005.

    Google Scholar 

  27. B. Mobasher, R. Burke, and J. Sandvig. Model-based collaborative filtering as a defense against profile injection attacks. AAAI Conference, Vol. 6, p. 1388, 2006.

    Google Scholar 

  28. P. Resnick and R. Sami. The influence limiter: provably manipulation-resistant recommender systems. ACM Conference on Recommender Systems, pp. 25–32, 2007.

    Google Scholar 

  29. P. Resnick and R. Sami. The information cost of manipulation resistance in recommender systems. ACM Conference on Recommender Systems, pp. 147–154, 2008.

    Google Scholar 

  30. P. Rousseeuw and A. Leroy. Robust regression and outlier detection John Wiley and Sons, 2005.

    Google Scholar 

  31. J. Sandvig, B. Mobasher, and R. Burke. Robustness of collaborative recommendation based on association rule mining. ACM Conference on Recommender Systems, pp. 105–12, 2007.

    Google Scholar 

  32. J. Sandvig, B. Mobasher, and R. Burke. A survey of collaborative recommendation and the robustness of model-based algorithms. IEEE Data Engineering Bulletin, 31(2), pp. 3–13, 2008.

    Google Scholar 

  33. X. Su, H. Zeng, and Z. Chen. Finding group shilling in recommendation system. World Wide Web Conference, pp. 960–961, 2005.

    Google Scholar 

  34. B. van Roy and X. Yan. Manipulation-resistant collaborative filtering systems. ACM Conference on Recommender Systems, pp. 165–172, 2009.

    Google Scholar 

  35. L. von Ahn, M. Blum, N. Hopper, and J. Langford. CAPTCHA: Using hard AI problems for security. Advances in Cryptology – EUROCRYPT, pp. 294–311, 2003.

    Google Scholar 

  36. C. Williams, B. Mobasher, and R. Burke. Defending recommender systems: detection of profile injection attacks. Service Oriented Computing and Applications, 1(3), pp. 157–170, 2007.

    Article  Google Scholar 

  37. C. Williams, B. Mobasher, R. Burke, J. Sandvig, and R. Bhaumik. Detection of obfuscated attacks in collaborative recommender systems. ECAI Workshop on Recommender Systems, 2006.

    Google Scholar 

  38. S. Zhang, A. Chakrabarti, J. Ford, and F. Makedon. Attack detection in time series for recommender systems. ACM KDD Conference, pp. 809–814, 2006.

    Google Scholar 

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Authors and Affiliations

  1. IBM T.J. Watson Research Center, Yorktown Heights, NY, USA

    Charu C. Aggarwal

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  1. Charu C. Aggarwal
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© 2016 Springer International Publishing Switzerland

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Aggarwal, C.C. (2016). Attack-Resistant Recommender Systems. In: Recommender Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-29659-3_12

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  • DOI: https://doi.org/10.1007/978-3-319-29659-3_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29657-9

  • Online ISBN: 978-3-319-29659-3

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