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Latent Semantic Models for Collaborative filtering
| Venue: | ACM Trans. Information Systems |
| Citations: | 331 - 1 self |
BibTeX
@ARTICLE{Hofmann_latentsemantic,
author = {Thomas Hofmann},
title = {Latent Semantic Models for Collaborative filtering},
journal = {ACM Trans. Information Systems},
year = {},
pages = {89--115}
}
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Abstract
Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, that is, a database of available user preferences. In this article, we describe a new family of model-based algorithms designed for this task. These algorithms rely on a statistical modelling technique that introduces latent class variables in a mixture model setting to discover user communities and prototypical interest profiles. We investigate several variations to deal with discrete and continuous response variables as well as with different objective functions. The main advantages of this technique over standard memory-based methods are higher accuracy, constant time prediction, and an explicit and compact model representation. The latter can also be used to mine for user communitites. The experimental evaluation shows that substantial improvements in accucracy over existing methods and published results can be obtained.
Keyphrases
latent semantic model collaborative filtering main advantage several variation user preference constant time prediction substantial improvement continuous response variable available user preference new family mixture model user communitites predictive model standard memory-based method experimental evaluation show user community community data model-based algorithm prototypical interest profile statistical modelling technique different objective function latent class variable collaborative filtering aim compact model representation
