Clustering in large graphs and matrices
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Fast computation of low rank matrix approximations
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
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
Recommendation Systems: A Probabilistic Analysis
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Noncryptographic Selection Protocols
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Fast Monte-Carlo Algorithms for Approximate Matrix Multiplication
FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
Using mixture models for collaborative filtering
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Improved recommendation systems
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Collaborate with strangers to find own preferences
Proceedings of the seventeenth annual ACM symposium on Parallelism in algorithms and architectures
Tell me who I am: an interactive recommendation system
Proceedings of the eighteenth annual ACM symposium on Parallelism in algorithms and architectures
SybilLimit: A Near-Optimal Social Network Defense against Sybil Attacks
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
SybilGuard: defending against sybil attacks via social networks
IEEE/ACM Transactions on Networking (TON)
Tell Me Who I Am: An Interactive Recommendation System
Theory of Computing Systems - Special Issue: Symposium on Parallelism in Algorithms and Architectures 2006; Guest Editors: Robert Kleinberg and Christian Scheideler
Finding similar users in social networks: extended abstract
Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures
DSybil: Optimal Sybil-Resistance for Recommendation Systems
SP '09 Proceedings of the 2009 30th IEEE Symposium on Security and Privacy
Recommender systems with non-binary grades
Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures
Improved collaborative filtering
ISAAC'11 Proceedings of the 22nd international conference on Algorithms and Computation
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Consider a set of players that are interested in collectively evaluating a set of objects. We develop a collaborative scoring protocol in which each player evaluates a subset of the objects, after which we can accurately predict each players' individual opinion of the remaining objects. The accuracy of the predictions is near optimal, depending on the number of objects evaluated by each player and the correlation among the players' preferences. A key novelty is the ability to tolerate malicious players. Surprisingly, the malicious players cause no (asymptotic) loss of accuracy in the predictions. In fact, our algorithm improves in both performance and accuracy over prior state-of-the-art collaborative scoring protocols that provided no robustness to malicious disruption.