| The maximum entropy principle in information retrieval |
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Annual ACM Conference on Research and Development in Information Retrieval
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Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
table of contents
Palazzo dei Congressi, Pisa, Italy
Pages: 269 - 274
Year of Publication: 1986
ISBN:0-89791-187-3
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Downloads (6 Weeks): 6, Downloads (12 Months): 89, Citation Count: 7
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 ABSTRACT
Applications, assumptions and properties of the maximum entropy principle are discussed. The maximum entropy principle integrates prior estimates of relevance with the observed distribution of term combinations. The result may be a reordering of the segments of a database, compared to a naive estimate. Numerical examples obtained by solution of the non-linear equations for the dual variables are presented and discussed.
* Supported in part by the National Science Foundation under grant IST-8318630.
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.
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COOP83
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COOPER, 9. S. "Exploiting the maximum entropy principle to increase retrieval effectiveness", J. Am. Soc. Inf. Sci., v34nl pp31-39 (1983 ). .
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COOP82
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COOPER, W. S. and HUIZINGA, P. "The maximum entropy principle and its application to the desiKn of prnbabillstic retrieval system", Inf. Technol: Res. &Dev,, vln2pp99-112 (1982).
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YU83
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CITED BY 7
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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
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