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
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Dependency Networks for Collaborative Filtering and Data Visualization
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Support Vector Machines for Polycategorical Classification
ECML '02 Proceedings of the 13th European Conference on Machine Learning
International Journal of Approximate Reasoning
A proposal for news recommendation based on clustering techniques
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
Top-N news recommendations in digital newspapers
Knowledge-Based Systems
Exploiting probabilistic latent information for the construction of community web directories
UM'05 Proceedings of the 10th international conference on User Modeling
A latent model for collaborative filtering
International Journal of Approximate Reasoning
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Recommender systems make use of a database of user ratings to generate personalized recommendations and help people to find relevant products, items, or documents. In this paper, we present a probabilistic, model-based framework for user ratings based on a novel collaborative filtering technique that performs an automatic decomposition of user preferences. Our approach has several benefits, including highly accurate predictions, task-optimized model learning, mining of interest groups and patterns, as well as a highly efficient and scalable computation of predictions and recommendation lists.