Latent Class Models for Collaborative Filtering
Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence. Machine Learning. 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.
@inproceedings{ijcai1999p98,
title = {Latent Class Models for Collaborative Filtering},
author = {Hofmann, Thomas and Puzicha, Jan},
booktitle = {Proceedings of the Sixteenth International Joint Conference on
Artificial Intelligence, {IJCAI-99}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Thomas Dean},
pages = {688--692},
year = {1999},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1999/98},
}
TY - CPAPER TI - Latent Class Models for Collaborative Filtering AU - Hofmann, Thomas AU - Puzicha, Jan T2 - Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI-99 PB - International Joint Conferences on Artificial Intelligence PY - 1999 SP - 688 EP - 692 UR - https://ijcai.org/proceedings/1999/98 ER -Copied
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