Recommendations without user preferences

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Recommendations without user preferences: a natural language processing approach

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International Conference on Intelligent User Interfaces archive
Proceedings of the 8th international conference on Intelligent user interfaces table of contents

Miami, Florida, USA

POSTER SESSION: Accepted Posters table of contents

Pages: 242 - 244

Year of Publication: 2003

ISBN:1-58113-586-6

Authors

Michael Fleischman	USC Information Science Institute, Marina del Rey, CA
Eduard Hovy	USC Information Science Institute, Marina del Rey, CA

Sponsors

ACM: Association for Computing Machinery
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 5, Downloads (12 Months): 18, Citation Count: 2

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abstract references cited by index terms collaborative colleagues

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ABSTRACT

We examine the problems with automated recommendation systems when information about user preferences is limited. We equate the problem to one of content similarity measurement and apply techniques from Natural Language Processing to the domain of movie recommendation. We describe two algorithms, a naïve word-space approach and a more sophisticated approach using topic signatures, and evaluate their performance compared to baseline, gold standard, and commercial systems.

REFERENCES

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.

	1	Marko Balabanović , Yoav Shoham, Fab: content-based, collaborative recommendation, Communications of the ACM, v.40 n.3, p.66-72, March 1997 [doi>10.1145/245108.245124]
	2	Fleischman, M. Text Classification in Reduced Dimensions using Topic Signatures and Hierarchical Topic Clustering, Masters Project, University of Southern California, 2002.
	3	Daniel Jurafsky , James H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, Prentice Hall PTR, Upper Saddle River, NJ, 2000
	4	Chin-Yew Lin , Eduard Hovy, The automated acquisition of topic signatures for text summarization, Proceedings of the 18th conference on Computational linguistics, p.495-501, July 31-August 04, 2000, Saarbrücken, Germany [doi>10.3115/990820.990892]
	5	Prem Melville , Raymod J. Mooney , Ramadass Nagarajan, Content-boosted collaborative filtering for improved recommendations, Eighteenth national conference on Artificial intelligence, p.187-192, July 28-August 01, 2002, Edmonton, Alberta, Canada
	6	Al Mamunur Rashid , Istvan Albert , Dan Cosley , Shyong K. Lam , Sean M. McNee , Joseph A. Konstan , John Riedl, Getting to know you: learning new user preferences in recommender systems, Proceedings of the 7th international conference on Intelligent user interfaces, January 13-16, 2002, San Francisco, California, USA [doi>10.1145/502716.502737]

CITED BY 2

	Edward Shen , Henry Lieberman , Francis Lam, What am I gonna wear?: scenario-oriented recommendation, Proceedings of the 12th international conference on Intelligent user interfaces, January 28-31, 2007, Honolulu, Hawaii, USA
	Mei-Hua Hsu, Proposing an ESL recommender teaching and learning system, Expert Systems with Applications: An International Journal, v.34 n.3, p.2102-2110, April, 2008