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)
Communications of the ACM
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Analyzing the economic efficiency of eBay-like online reputation reporting mechanisms
Proceedings of the 3rd ACM conference on Electronic Commerce
Competitive recommendation systems
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
The Nonstochastic Multiarmed Bandit Problem
SIAM Journal on Computing
Latent Class Models for Collaborative Filtering
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
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
Collaborate with strangers to find own preferences
Proceedings of the seventeenth annual ACM symposium on Parallelism in algorithms and architectures
Anytime algorithms for multi-armed bandit problems
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Tell me who I am: an interactive recommendation system
Proceedings of the eighteenth annual ACM symposium on Parallelism in algorithms and architectures
Competitive collaborative learning
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Proceedings of the sixteenth international conference on Architectural support for programming languages and operating systems
Distributed weighted stable marriage problem
SIROCCO'10 Proceedings of the 17th international conference on Structural Information and Communication Complexity
Friend or frenemy?: predicting signed ties in social networks
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Improving e-learning communities through optimal composition of multidisciplinary learning groups
Computers in Human Behavior
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Intuitively, it is clear that trust or shared taste enables a community of users to make better decisions over time, by learning cooperatively and avoiding one another's mistakes. However, it is also clear that the presence of malicious, dishonest users in the community threatens the usefulness of such collaborative learning processes. We investigate this issue by developing algorithms for a multi-user online learning problem in which each user makes a sequence of decisions about selecting products or resources. Our model, which generalizes the adversarial multi-armed bandit problem, is characterized by two key features:(1)The quality of the products or resources may vary over time. (2)Some of the users in the system may be dishonest, Byzantine agents. Decision problems with these features underlie applications such as reputation and recommendation systems in e-commerce, and resource location systems in peer-to-peer networks. Assuming the number of honest users is at least a constant fraction of the number of resources, and that the honest users can be partitioned into groups such that individuals in a group make identical assessments of resources, we present an algorithm whose expected regret per user is linear in the number of groups and only logarithmic in the number of resources. This bound compares favorably with the naive approach in which each user ignores feedback from peers and chooses resources using a multi-armed bandit algorithm; in this case the expected regret per user would be polynomial in the number of resources.