Thomas Hofmann
Abstract
Collaborative filtering aims at learning predictive models of user preferences, interests or behavior from community data, that is, a database of available user preferences. In this article, we describe a new family of model-based algorithms designed for this task. These algorithms rely on a statistical modelling technique that introduces latent class variables in a mixture model setting to discover user communities and prototypical interest profiles. We investigate several variations to deal with discrete and continuous response variables as well as with different objective functions. The main advantages of this technique over standard memory-based methods are higher accuracy, constant time prediction, and an explicit and compact model representation. The latter can also be used to mine for user communitites. The experimental evaluation shows that substantial improvements in accucracy over existing methods and published results can be obtained.
- Basu, C., Hirsh, H., and Cohen, W. 1998. Recommendation as classification: Using social and content-based information in recommendation. In Proceedings of the Recommender System Workshop. 11--15.]] Google Scholar
- Blei, D. M., Ng, A. Y., and Jordan, M. I. 2002. Latent dirichlet allocation. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, Mass.]]Google Scholar
- Breese, J. S., Heckerman, D., and Kardie, C. 1998. Empiricial analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainity in Aritificial Intelligence. 43--52.]]Google Scholar
- Canny, J. 2002. Collaborative filtering with privacy. In Proceedings of the IEEE Symposium on Security and Privacy. IEEE Computer Society Press, Los Alamitos, Calif., pp. 45--57.]] Google Scholar
- Chien, Y.-H. and George, E. 1999. A Bayesian model for collaborative filtering. In Online Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics.]]Google Scholar
- Cover, T. M. and Thomas, J. A. 1991. Information Theory. Wiley, New York.]]Google Scholar
- Deerwester, S. C., Dumais, S. T., Landauer, T. K., Furnas, G. W., and Harshman, R. A. 1990. Indexing by latent semantic analysis. J. ASIS 41, 6, 391--407.]]Google Scholar
- Dempster, A., Laird, N., and Rubin, D. 1977. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statist. Soc. B 39, 1--38.]]Google Scholar
- EachMovie. www.research.digital.com/src/eachmovie/.]]Google Scholar
- Freund, Y., Iyer, R., Schapire, R. E., and Singer, Y. 1998. An efficient boosting algorithm for combining preferences. In Proceedings of ICML-98, 15th International Conference on Machine Learning, J. W. Shavlik, Ed. Morgan-Kaufmann, San Francisco, Calif., 170--178.]] Google Scholar
- Goldberg, D., Nichols, D., Oki, B., and Terry, D. 1992. Using collabrorative filtering to weave an information tapestry. Commun. ACM 35, 12, 61--70.]] Google Scholar
- Goldberg, K., Roeder, T., Gupta, D., and Perkins, C. 2001. Eigentaste: A constant-time collaborative filtering algorithm. Inf. Retr. 4, 2, 133--151.]] Google Scholar
- Heckerman, D., Chickering, D. M., Meek, C., Rounthwaite, R., and Kadie, C. M. 2000. Dependency networks for inference, collaborative filtering, and data visualization. J. Mach. Learn. Res. 1, 49--75.]] Google Scholar
- Herlocker, J., Konstan, J., Borchers, A., and Riedl, J. 1999. An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd ACM-SIGIR International Conference on Research and Development in Information Retrieval (Berkeley, Calif.). ACM, New York.]] Google Scholar
- Hofmann, T. 1999. Probabilistic latent semantic indexing. In Proceedings of the 22nd ACM-SIGIR International Conference on Research and Development in Information Retrieval (Berkeley, Calif.), ACM, New York, 50--57.]] Google Scholar
- Hofmann, T. 2001a. Unsupervised learning by probabilistic latent semantic analysis. Mach. Learn. J. 42, 1, 177--196.]] Google Scholar
- Hofmann, T. 2001b. What people (don't) want. In Proceedings of the European Conference on Machine Learning (ECML).]] Google Scholar
- Hofmann, T. and Puzicha, J. 1999. Latent class models for collaborative filtering. In Proceedings of the International Joint Conference in Artificial Intelligence.]] Google Scholar
- Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., and Riedl, J. 1997. Grouplens: Applying collaborative filtering to usenet news. Commun. ACM 40, 3, 77--87.]] Google Scholar
- Minka, T. and Lafferty, J. 2002. Expectation-propagation for the generative aspect model. In Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence.]]Google Scholar
- Neal, R. and Hinton, G. 1998. A view of the EM algorithm that justifies incremental, sparse, and other variants. In Learning in Graphical Models, M. I. Jordan, Ed. Kluwer.]] Google Scholar
- Pereira, F. C. N., Tishby, N., and Lee, L. 1993. Distributional clustering of English words. In Meeting of the Association for Computational Linguistics. 183--190.]] Google Scholar
- Resnik, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. 1994. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the ACM, Conference on Computer Supported Cooperative Work. 175--186.]] Google Scholar
- Sarwar, B. M., Karypis, G., Konstan, J. A., and Riedl, J. 2000. Application of dimensionality reduction in recommender system---A case study. In Proceedings of the ACM WebKDD 2000 Web Mining for E-Commerce Workshop. ACM, New York.]]Google Scholar
- Sarwar, B. M., Karypis, G., Konstan, J. A., and Reidl, J. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the World Wide Web Conference (WWWC10). 285--295.]] Google Scholar
- Shardanand, U. and Maes, P. 1995. Social information filtering: Algorithms for automating "word of mouth". In Proceedings of ACM CHI'95 Conference on Human Factors in Computing Systems. Vol. 1. ACM, New York, 210--217.]] Google Scholar
- Ungar, L. and Foster, D. 1998. Clustering methods for collaborative filtering. In Proceedings of the Workshop on Recommendation Systems. AAAI Press, Menlo Park, Calif.]]Google Scholar
Index Terms
- Latent semantic models for collaborative filtering
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