Learning in the presence of malicious errors
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Competitive recommendation systems
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Promoting Recommendations: An Attack on Collaborative Filtering
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Recommendation Systems: A Probabilistic Analysis
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Proceedings of the 10th international conference on Intelligent user interfaces
Trust no one: evaluating trust-based filtering for recommenders
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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Collaborative recommendation has emerged as an effective technique for a personalized information access. However, there has been relatively little theoretical analysis of the conditions under which the technique is effective. We analyze the robustness of collaborative recommendation: the ability to make recommendations despite (possibly intentional) noisy product ratings. We formalize robustness in machine learning terms, develop two theoretically justified models of robustness, and evaluate the models on real-world data. Our investigation is both practically relevant for enterprises wondering whether collaborative recommendation leaves their marketing operations open to attack, and theoretically interesting for the light it sheds on a comprehensive theory of collaborative recommendation.