Matrix Factorization Techniques for Recommender Systems

Authors:
Yehuda Koren;Robert Bell;Chris Volinsky
Affiliations:
Yahoo Research;AT&TLabs;AT&TLabs
Venue:
Computer
Year:
2009

Citing 0
Cited 275

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Expert Systems with Applications: An International Journal
Experimental evaluation of context-dependent collaborative filtering using item splitting

User Modeling and User-Adapted Interaction
Nonadditive similarity-based single-layer perceptron for multi-criteria collaborative filtering

Neurocomputing
Semi-sparse algorithm based on multi-layer optimization for recommender system

The Journal of Supercomputing
The neglected user in music information retrieval research

Journal of Intelligent Information Systems
TWILITE: A recommendation system for Twitter using a probabilistic model based on latent Dirichlet allocation

Information Systems
Tutorial on application-oriented evaluation of recommendation systems

AI Communications
Detecting inappropriate access to electronic health records using collaborative filtering

Machine Learning

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Abstract

As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest-neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.