---
title: "Latent Class Models for Collaborative Filtering"
authors:
  - "Thomas Hofmann"
  - "Jan Puzicha"
doi: ""
url: "https://ijcai.org/proceedings/1999/98"
pdf: "https://ijcai.org/proceedings/1999/0098.pdf"
venue: "Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence"
year: 1999
pages: "688-692"
track: "Machine Learning"
keywords:
---

# Latent Class Models for Collaborative Filtering

Thomas Hofmann, Jan Puzicha

*Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence*, pages 688-692.

## Abstract

This paper presents a statistical approach to collaborative filtering and investigates the use of lat e nt class mod els 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.