Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
PHOAKS: a system for sharing recommendations
Communications of the ACM
Fab: content-based, collaborative recommendation
Communications of the ACM
Siteseer: personalized navigation for the Web
Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Communications of the ACM
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Collaborative Reputation Mechanisms in Electronic Marketplaces
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Partnership reviewing: a cooperative approach for peer review of complex educational resources
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
The convergence of digital libraries and the peer-review process
Journal of Information Science
A survey of trust and reputation systems for online service provision
Decision Support Systems
Collaborative Filtering Using Dual Information Sources
IEEE Intelligent Systems
Detecting reviewer bias through web-based association mining
Proceedings of the 2nd ACM workshop on Information credibility on the web
RATE: A Review of Reviewers in a Manuscript Review Process
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Multidimensional credibility model for neighbor selection in collaborative recommendation
Expert Systems with Applications: An International Journal
Source credibility model for neighbor selection in collaborative web content recommendation
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
Sequence-based trust in collaborative filtering for document recommendation
International Journal of Human-Computer Studies
Collaborative quality filtering: establishing consensus or recovering ground truth?
WebKDD'04 Proceedings of the 6th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
Quality and Leniency in Online Collaborative Rating Systems
ACM Transactions on the Web (TWEB)
Opinion filtered recommendation trust model in peer-to-peer networks
AP2PC'04 Proceedings of the Third international conference on Agents and Peer-to-Peer Computing
Information Sciences: an International Journal
To whom should I listen? Finding reputable reviewers in opinion-sharing communities
Decision Support Systems
Trust, distrust and lack of confidence of users in online social media-sharing communities
Knowledge-Based Systems
Novel personal and group-based trust models in collaborative filtering for document recommendation
Information Sciences: an International Journal
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The current system for scholarly information dissemination may be amen able to significant improvement. In particular, going from the current system of journal publication to one of self-distributed documents offers significant cost and timeliness advantages. A major concern with such alternatives is how to provide the value currently afforded by the peer review system.Here we propose a mechanism that could plausibly supply such value. In the peer review system, papers are judged meritorious if good reviewers give them good reviews. In its place, we propose a collaborative filtering algorithm which automatically rates reviewers, and incorporates the quality of the reviewer into the metric of merit for the paper. Such a system seems to provide all the benefits of the current peer review system, while at the same time being much more flexible.We have implemented a number of parameterized variations of this algorithm, and tested them on data available from a quite different application. Our initial experiments suggest that the algorithm is in fact ranking reviewers reasonably.