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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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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.
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.
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.
R. Bhaumik, R. Burke, snd B. Mobasher. Crawling Attacks Against Web-based Recommender Systems. International Conference on Data Mining (DMIN), pp. 183–189, 2007.
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.
R. Burke, M. O’Mahony, and N. Hurley. Robust collaborative recommendation. Recommender Systems Handbook, Springer, pp. 805–835, 2011.
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.
R. Burke, B. Mobasher, and R. Bhaumik. Limited knowledge shilling attacks in collaborative filtering systems. IJCAI Workshop in Intelligent Techniques for Personalization, 2005.
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.
E. Candes, X. Li, Y. Ma, and J. Wright. Robust principal component analysis?. Journal of the ACM (JACM), 58(3), 11, 2011.
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.
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. CrossRef
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. CrossRef
I. Jolliffe. Principal component analysis, 2nd edition, Springer, 2002.
S. Lam and J. Riedl. Shilling recommender systems for fun and profit. World Wide Web Conference, pp. 393–402, 2004.
M. O’Mahony, N. Hurley, and G. Silvestre. Promoting recommendations: An attack on collaborative filtering. Database and Expert Systems Applications, pp. 494–503, 2002.
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.
M. O’Mahony, N. Hurley, G. Silvestre. Recommender systems: Attack types and strategies. National Conference on Artificial Intelligence (AAAI), pp. 334–339, 2005.
B. Mehta, and T. Hofmann. A survey of attack-resistant collaborative filtering algorithms. IEEE Data Enginerring Bulletin, 31(2), pp. 14–22, 2008.
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.
B. Mehta, T. Hofmann, and W. Nejdl. Robust collaborative filtering. ACM Conference on Recommender Systems, pp. 49–56, 2007.
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. CrossRef
B. Mehta and W. Nejdl. Attack resistant collaborative filtering. ACM SIGIR Conference, pp. 75–82, 2008.
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.
B. Mobasher, R. Burke, R. Bhaumik, and C. Williams. Effective attack models for shilling item-based collaborative filtering systems. WebKDD Workshop, 2005.
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.
P. Resnick and R. Sami. The influence limiter: provably manipulation-resistant recommender systems. ACM Conference on Recommender Systems, pp. 25–32, 2007.
P. Resnick and R. Sami. The information cost of manipulation resistance in recommender systems. ACM Conference on Recommender Systems, pp. 147–154, 2008.
P. Rousseeuw and A. Leroy. Robust regression and outlier detection John Wiley and Sons, 2005.
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.
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.
X. Su, H. Zeng, and Z. Chen. Finding group shilling in recommendation system. World Wide Web Conference, pp. 960–961, 2005.
B. van Roy and X. Yan. Manipulation-resistant collaborative filtering systems. ACM Conference on Recommender Systems, pp. 165–172, 2009.
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.
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. CrossRef
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.
S. Zhang, A. Chakrabarti, J. Ford, and F. Makedon. Attack detection in time series for recommender systems. ACM KDD Conference, pp. 809–814, 2006.
- Attack-Resistant Recommender Systems
Charu C. Aggarwal
- Chapter 12
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