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top of pageABSTRACT

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.
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Thomas Hofmann Thomas Hofmann

homepage
thomas_hofmannatacm.org
Bibliometrics: publication history
Publication years1995-2016
Publication count73
Citation Count4,143
Available for download29
Downloads (6 Weeks)249
Downloads (12 Months)3,484
Downloads (cumulative)39,460
Average downloads per article1,360.69
Average citations per article56.75
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top of pageREFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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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.
 
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Cover, T. M. and Thomas, J. A. 1991. Information Theory. Wiley, New York.
 
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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.
 
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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.
 
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EachMovie. www.research.digital.com/src/eachmovie/.
 
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Hofmann, T. 2001b. What people (don't) want. In Proceedings of the European Conference on Machine Learning (ECML).
 
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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.
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Ungar, L. and Foster, D. 1998. Clustering methods for collaborative filtering. In Proceedings of the Workshop on Recommendation Systems. AAAI Press, Menlo Park, Calif.

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342 Citations

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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The ACM Computing Classification System (CCS rev.2012)

Note: Larger/Darker text within each node indicates a higher relevance of the materials to the taxonomic classification.

top of pagePUBLICATION

Title ACM Transactions on Information Systems (TOIS) TOIS Homepage table of contents archive
Volume 22 Issue 1, January 2004
Pages 89-115
Publication Date2004-01-01 (yyyy-mm-dd)
PublisherACM New York, NY, USA
ISSN: 1046-8188 EISSN: 1558-2868 doi>10.1145/963770.963774

APPEARS IN
Digital Content Digital Content: The ACM Collection on Digital Content

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top of pageTable of Contents

ACM Transactions on Information Systems (TOIS)

Volume 22 Issue 1, January 2004

Table of Contents
Introduction to recommender systems: Algorithms and Evaluation
Joseph A. Konstan
Pages: 1-4
doi>10.1145/963770.963771
Full text: PDFPDF
Evaluating collaborative filtering recommender systems
Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, John T. Riedl
Pages: 5-53
doi>10.1145/963770.963772
Full text: PDFPDF

Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being ...
expand
Ontological user profiling in recommender systems
Stuart E. Middleton, Nigel R. Shadbolt, David C. De Roure
Pages: 54-88
doi>10.1145/963770.963773
Full text: PDFPDF

We explore a novel ontological approach to user profiling within recommender systems, working on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively ...
expand
Latent semantic models for collaborative filtering
Thomas Hofmann
Pages: 89-115
doi>10.1145/963770.963774
Full text: PDFPDF

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 ...
expand
Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering
Zan Huang, Hsinchun Chen, Daniel Zeng
Pages: 116-142
doi>10.1145/963770.963775
Full text: PDFPDF

Recommender systems are being widely applied in many application settings to suggest products, services, and information items to potential consumers. Collaborative filtering, the most successful recommendation approach, makes recommendations based on ...
expand
Item-based top-N recommendation algorithms
Mukund Deshpande, George Karypis
Pages: 143-177
doi>10.1145/963770.963776
Full text: PDFPDF

The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems---a personalized information filtering technology used to identify a set of items that will be of interest to a certain ...
expand

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