Article

Restricted Boltzmann machines for collaborative filtering

Authors:

Ruslan Salakhutdinov,

Geoffrey HintonAuthors Info & Claims

ICML '07: Proceedings of the 24th international conference on Machine learning

Pages 791 - 798

https://doi.org/10.1145/1273496.1273596

Published: 20 June 2007 Publication History

Abstract

Most of the existing approaches to collaborative filtering cannot handle very large data sets. In this paper we show how a class of two-layer undirected graphical models, called Restricted Boltzmann Machines (RBM's), can be used to model tabular data, such as user's ratings of movies. We present efficient learning and inference procedures for this class of models and demonstrate that RBM's can be successfully applied to the Netflix data set, containing over 100 million user/movie ratings. We also show that RBM's slightly outperform carefully-tuned SVD models. When the predictions of multiple RBM models and multiple SVD models are linearly combined, we achieve an error rate that is well over 6% better than the score of Netflix's own system.

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Cited By

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Restricted Boltzmann machines for collaborative filtering
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Published In

cover image ACM Other conferences

ICML '07: Proceedings of the 24th international conference on Machine learning

June 2007

1233 pages

ISBN:9781595937933

DOI:10.1145/1273496

Editor:
Zoubin Ghahramani
University of Cambridge, United Kingdom

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 20 June 2007

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ICML '07 & ILP '07

Sponsor:

ICML '07 & ILP '07: The 24th Annual International Conference on Machine Learning held in conjunction with the 2007 International Conference on Inductive Logic Programming

June 20 - 24, 2007

Oregon, Corvalis, USA

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Quiroz Monnens SPeters CHesselink LSmeets KEnglitz B(2024)The recurrent temporal restricted Boltzmann machine captures neural assembly dynamics in whole-brain activityeLife10.7554/eLife.9848913Online publication date: 5-Nov-2024
https://doi.org/10.7554/eLife.98489
Ceyda Yelgel ÖYelgel C(2024)The Role of Machine Learning Methods for Renewable Energy ForecastingAdvances in Energy Recovery and Efficiency Technologies [Working Title]10.5772/intechopen.1007556Online publication date: 28-Oct-2024
https://doi.org/10.5772/intechopen.1007556
Sanjay Suryawanshi N(2024)Predicting Consumer Behavior in E-Commerce Using Recommendation SystemsInternational Journal of Innovative Science and Research Technology (IJISRT)10.38124/ijisrt/IJISRT19SEP1550(806-813)Online publication date: 21-Oct-2024
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