Impact of relevance measures on the robustness and accuracy of collaborative filtering

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4655 LNCS, Issue , 2007, Pages 99-108

  Sandvig, J J ^a   Mobasher, Bamshad ^a   Burke, Robin ^a  

^a DEPAUL UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; PROBLEM SOLVING; SECURITY SYSTEMS; USER INTERFACES;

COLLABORATIVE FILTERING; PROFILE INJECTION ATTACKS; RECOMMENDER SYSTEMS;

COMPUTER SUPPORTED COOPERATIVE WORK;

EID: 38049066311     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74563-1_10     Document Type: Conference Paper

References (10)

1. Lam, S., Riedl, J.: Shilling recommender systems for fun and profit. In: Proceedings of the 13th International WWW Conference, New York (May 2004)
2. O'Mahony, M., Hurley, N., Kushmerick, N., Silvestre, G.: Collaborative recommendation: A robustness analysis. ACM Transactions on Internet Technology 4(4), 344-377 (2004)
3. 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(4) (2007)
4. Herlocker, J., Konstan, J., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd ACM Conference on Research and Development in Information Retrieval (SIGIR'99), Berkeley, CA (August 1999)
5. O'Donovan, J., Smyth, B.: Trust in recommender systems. In: Proceedings of the 10th International Conference on Intelligent User Interfaces (EC'04), pp. 167-174. ACM Press, New York (2005)
6. Mobasher, B., Burke, R., Sandvig, J.J.: Model-based collaborative filtering as a defense against profile injection attacks. In: Proceedings of the 21st National Conference on Artificial Intelligence, pp. 1388-1393. AAAI Press, Stanford, California (July 2006)
7. Breese, J., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Uncertainty in Artificial Intelligence. Proceedings of the Fourteenth Conference, New Orleans, LA, pp. 43-53. Morgan Kaufman, Seattle, Washington (1998)
8. Williams, C., Bhaumik, R., Burke, R., Mobasher, B.: The impact of attack profile classification on the robustness of collaborative recommendation. In: Proceedings of the 2006 WebKDD Workshop, held at ACM SIGKDD Conference on Data Mining and Knowledge Discovery (KDD'06), Philadelphia (August 2006)
9. O'Donovan, J., Smyth, B.: Is trust robust?; An analysis of trust-based recommendation. In: Proceedings of the 5th ACM Conference on Electronic Commerce (EC'04), pp. 101-108. ACM Press, New York (2006)
10. Herlocker, J., Konstan, J., Tervin, L.G., Riedl, J.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22(1), 5-53 (2004)