Latent class models for collaborative filtering

  • Authors:
  • Thomas Hofmann;Jan Puzicha

  • Affiliations:
  • CS Division, UC Berkeley and International CS Institute, Berkeley, CA;Institut fur Informatik, University of Bonn, Bonn, Germany

  • Venue:
  • IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
  • Year:
  • 1999

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Abstract

This paper presents a statistical approach to collaborative filtering and investigates the use of latent class models for predicting individual choices and preferences based on observed preference behavior. Two models are discussed and compared: the aspect model, a probabilistic latent space model which models individual preferences as a convex combination of preference factors, and the two-sided clustering model, which simultaneously partitions persons and objects into clusters. We present EM algorithms for different variants of the aspect model and derive an approximate EM algorithm based on a variational principle for the two-sided clustering model. The benefits of the different models are experimentally investigated on a large movie data set.