Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Web document clustering: a feasibility demonstration
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Communications of the ACM
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
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
Introduction to Algorithms
A Social Mechanism of Reputation Management in Electronic Communities
CIA '00 Proceedings of the 4th International Workshop on Cooperative Information Agents IV, The Future of Information Agents in Cyberspace
The Eigentrust algorithm for reputation management in P2P networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
Gambling in a rigged casino: The adversarial multi-armed bandit problem
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Collaborative Reputation Mechanisms in Electronic Marketplaces
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Collaboration of untrusting peers with changing interests
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Improved recommendation systems
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Anytime algorithms for multi-armed bandit problems
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Fast convergence to Wardrop equilibria by adaptive sampling methods
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Online collaborative filtering with nearly optimal dynamic regret
Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
The influence limiter: provably manipulation-resistant recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Nonstochastic bandits: Countable decision set, unbounded costs and reactive environments
Theoretical Computer Science
The information cost of manipulation-resistance in recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Competitive collaborative learning
Journal of Computer and System Sciences
Gossip-based aggregation of trust in decentralized reputation systems
Autonomous Agents and Multi-Agent Systems
A brief survey of computational approaches in social computing
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Fast Convergence to Wardrop Equilibria by Adaptive Sampling Methods
SIAM Journal on Computing
A novel protocol for communicating reputation in p2p networks
iTrust'06 Proceedings of the 4th international conference on Trust Management
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We develop algorithms for a community of users to make decisions about selecting products or resources, in a model characterized by two key features:The quality of the products or resources may vary over time. Some of the users in the system may be dishonest, manipulating their actions in a Byzantine manner to achieve other goals. We formulate such learning tasks as an algorithmic problem based on the multi-armed bandit problem, but with a set of users (as opposed to a single user), of whom a constant fraction are honest and are partitioned into coalitions such that the users in a coalition perceive the same expected quality if they sample the same resource at the same time. Our main result exhibits an algorithm for this problem which converges in polylogarithmic time to a state in which the average regret (per honest user) is an arbitrarily small constant.