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2003
ACM

Collaborative filtering via gaussian probabilistic latent semantic analysis

9 years 2 months ago
Collaborative filtering via gaussian probabilistic latent semantic analysis
Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, i.e. a database of available user preferences. In this paper, we describe a new model-based algorithm designed for this task, which is based on a generalization of probabilistic latent semantic analysis to continuous-valued response variables. More specifically, we assume that the observed user ratings can be modeled as a mixture of user communities or interest groups, where users may participate probabilistically in one or more groups. Each community is characterized by a Gaussian distribution on the normalized ratings for each item. The normalization of ratings is performed in a user-specific manner to account for variations in absolute shift and variance of ratings. Experiments on the EachMovie data set show that the proposed approach compares favorably with other collaborative filtering techniques. Categories and Subject Descriptors H.3.3 [Information Stor...
Thomas Hofmann
Added 05 Jul 2010
Updated 05 Jul 2010
Type Conference
Year 2003
Where SIGIR
Authors Thomas Hofmann
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