]> BibSonomy publications for /bibtex/273ba7f4b8dde46a7895213dbb11b8722/wnpxrz http://www.bibsonomy.org/burst/bibtex/273ba7f4b8dde46a7895213dbb11b8722/wnpxrz BibSonomy BuRST Feed for /bibtex/273ba7f4b8dde46a7895213dbb11b8722/wnpxrz 2008-09-05T08:30:29+02:00 Latent semantic models for collaborative filtering http://www.bibsonomy.org/bibtex/273ba7f4b8dde46a7895213dbb11b8722/wnpxrz wnpxrz 2007-06-28T14:04:11+02:00 collaborative filtering latent lsi modeling semantic
Latent semantic models for collaborative filtering
Thomas Hofmann ACM Transactions on Information Systems (TOIS) 22 89--115 (2004)
to collaborative filtering latent lsi modeling semantic on 2007-06-28 14:04:11 ]]> ACM Transactions on Information Systems (TOIS)189--115Latent semantic models for collaborative filtering222004collaborative filtering latent lsi modeling semantic 2007-06-28 14:04:11.0Collaborative 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.