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Learning Collaborative Information Filters (1998)
| Venue: | In Proc. 15th International Conf. on Machine Learning |
| Citations: | 354 - 4 self |
BibTeX
@INPROCEEDINGS{Billsus98learningcollaborative,
author = {Daniel Billsus and Michael J. Pazzani},
title = {Learning Collaborative Information Filters},
booktitle = {In Proc. 15th International Conf. on Machine Learning},
year = {1998},
pages = {46--54},
publisher = {Morgan Kaufmann}
}
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Abstract
Predicting items a user would like on the basis of other users’ ratings for these items has become a well-established strategy adopted by many recommendation services on the Internet. Although this can be seen as a classification problem, algo-rithms proposed thus far do not draw on results from the ma-chine learning literature. We propose a representation for collaborative filtering tasks that allows the application of virtually any machine learning algorithm. We identify the shortcomings of current collaborative filtering techniques and propose the use of learning algorithms paired with feature extraction techniques that specifically address the limitations of previous approaches. Our best-performing algorithm is based on the singular value decomposition of an initial matrix of user ratings, exploiting latent structure that essentially eliminates the need for users to rate common items in order to become predictors for one another's preferences. We evaluate the proposed algorithm on a large database of user ratings for motion pictures and find that our approach significantly out-performs current collaborative filtering algorithms.
Keyphrases
collaborative information filter user rating singular value decomposition rate common item classification problem well-established strategy machine learning algorithm current collaborative filtering technique feature extraction technique previous approach many recommendation service ma-chine learning literature out-performs current collaborative filtering algorithm collaborative filtering task initial matrix best-performing algorithm latent structure motion picture large database
