The influence limiter: Provably manipulation-resistant recommender systems

RecSys'07: Proceedings of the 2007 ACM Conference on Recommender Systems

Volumn , Issue , 2007, Pages 25-32

  Resnick, Paul ^a   Sami, Rahul ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Manipulation resistance; Recommender systems; Shilling

Indexed keywords

LOSS FUNCTIONS; MANIPULATION-RESISTANCE; RECOMMENDER SYSTEMS; SHILLING;

ALGORITHMS; FUNCTIONS; OPTIMAL CONTROL SYSTEMS; OPTIMIZATION;

INFORMATION SYSTEMS;

EID: 42149166795     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1297231.1297236     Document Type: Conference Paper

References (27)

1. B. Awerbuch and R. D. Kleinberg. Competitive collaborative learning. In 18th Annual Conference on Learning Theory (COLT 2005), volume 3559 of LNAI, pages 233-248. Springer, 2005.
2. B. Awerbuch, B. Patt-Shamir, D. Peleg, and M. R. Tuttle. Improved recommendation systems. In ACM-SIAM Symposium on Discrete Algorithms, pages 1174-1183. SIAM, 2005.
3. R. Bhattacharjee and A. Goel. Algorithms and incentives for robust ranking. In Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA '07), 2007.
4. G. Brier. Verification of forecasts expressed in terms of probability. Monthly Weather Review, 78:1-3, 1950.
5. N. Cesa-Bianchi and G. Lugosi. Prediction, Learning, and Games. Cambridge University Press, 2006.
6. A. Cheng and E. Friedman. Sybilproof reputation mechanisms. In P2PECON '05: Proceeding of the 2005 ACM SIGCOMM workshop on Economics of peer-to-peer systems, pages 128-132, 2005.
7. P.-A. Chirita, W. Nejdl, and C. Zamfir. Preventing shilling attacks in online recommender systems. In WIDM 05, pages 67-74, 2005.
8. C. Dellarocas. Building trust online: The design of robust reputation reporting mechanisms in online trading communties. In Information Society or Information Economy? A combined perspective on the digital, era, pages 95-113. Idea, Book Publishing, 2003.
9. I. J. Good. Rational decisions. Journal of the Royal Statistical Society. Series B (Methodological), 14(1):107-114, 1952.
10. P. Grunwald and A. Dawid. Game theory, maximum entropy, minimum discrepancy and robust bayesian decision theory. Annals of Statistics, 32:1367-1433, 2004.
11. R. Hanson. Combinatorial information market design. Information Systems Frontiers, 5(1):107-119, 2003.
12. R. Hanson, R. Oprea, and D. Porter. Information aggregation and manipulation in an experimental market. Journal of Economic Behavior and Organization, page (to appear), 2006.
13. J. Herlocker, J. Konstan, and J. Riedl. An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Information Retrieval, 5:287-310, 2002.
14. S. D. Kamvar, M. T. Schlosser, and H. Garcia-Molina. The eigentrust algorithm for reputation management in P2P networks. In Prooceedings of WWW '03, pages 640-651, 2003.
15. S. K. Lam and J. Riedl. Shilling recommender systems for fun and profit. In Proceedings of WWW '04, pages 393-402, 2004.
16. R. Levien. Attack-Resistant Trust Metrics. PhD thesis, University of California, Berkeley, 2004.
17. P. Massa and B. Bhattacharjee. Using trust in recommender systems: An experimental analysis. In Proceedings of the 2nd International Conference on Trust Management, 2004.
18. B. Mehta, T. Hoffman, and P. Fankhauser. Lies and propaganda:detecting spam users in collaborative filtering. In Proceedings of IUI'07, 2007.
19. N. Miller, P. Resnick, and R. Zeckhauser. Eliciting honest feedback: The peer-prediction method. Management Science, 51(9): 1359-1373, 2005.
20. B. Mobasher, R. Burke, R. Bhaumik, and C. Williams. Towards trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Transactions on Internet Technology, 7(2):1-40, 2007.
21. J. O'Donovan and B. Smyth. Trust no one: Evaluating trust-based filtering for recommenders. In Proceedings of IJCAI'05, 2005.
22. M. O'Mahony, N. Hurley, and G. Silvestre. Promoting recommendations: An attack on collaborative filtering. In Proceedings of the 13th International Conference on Database and Expert System Applications, pages 494-503. Springer-Verlag, 2002.
23. M. P. O'Mahony, N. J. Hurley, and G. C. M. Silvestre. Detecting noise in recommender system databases. In Proceedings of the 2006 International Conference on Intelligent User Interfaces, pages 109-115, 2006.
24. A. M. Rashid, I. Albert, D. Cosley, S. K. Lam, S. M. McNee, J. A. Konstan, and J. Riedl. Getting to know you: learning new user preferences in recommender systems. In IUI '02, pages 127-134, 2002.
25. A. M. Rashid, G. Karypis, and J. Riedl. Influence in ratings-based recommender systems: An algorithm-independent approach. In Proceedings of the SIAM International Conference on Data Mining, 2005.
26. G. Shafer and V. Vovk. Probability and Finance: It's Only a Game! John Wiley and Sons, 2001.
27. L. von Ahn, M. Blum, N. Hopper, and J. Langford. CAPTCHA: Using Hard AI Problems for Security. In Proceedings of Eurocrypt 2003, 2003.