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
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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
An algorithm for automated rating of reviewers
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Collaborative Reputation Mechanisms in Electronic Marketplaces
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Learning from Incomplete Data
Objective quality ranking of computing journals
Communications of the ACM - Service-oriented computing
Introduction to recommender systems: Algorithms and Evaluation
ACM Transactions on Information Systems (TOIS)
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
WebKDD 2004: web mining and web usage analysis post-workshop report
ACM SIGKDD Explorations Newsletter
Evaluation of attribute-aware recommender system algorithms on data with varying characteristics
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Quality and Leniency in Online Collaborative Rating Systems
ACM Transactions on the Web (TWEB)
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We present a algorithm based on factor analysis for performing collaborative quality filtering (CQF). Unlike previous approaches to CQF, which estimate the consensus opinion of a group of reviewers, our algorithm uses a generative model of the review process to estimate the latent intrinsic quality of the items under reviews. We run several tests that demonstrate that consensus and intrinsic quality are, in fact different and unrelated aspects of quality. These results suggest that asymptotic consensus, which purports to model peer review, is, in fact, not recovering the ground truth quality of reviewed items.