Mean-field approach to a probabilistic model in information retrieval

Bin Wu, K. Y.Michael Wong, David Bodoff

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We study an explicit parametric model of documents, queries, and relevancy assessment for Information Retrieval (IR). Mean-field methods are applied to analyze the model and derive efficient practical algorithms to estimate the parameters in the problem. The hyperparameters are estimated by a fast approximate leave-one-out cross-validation procedure based on the cavity method. The algorithm is further evaluated on several benchmark databases by comparing with standard algorithms in IR.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002
PublisherNeural information processing systems foundation
ISBN (Print)0262025507, 9780262025508
Publication statusPublished - Jan 1 2003
Externally publishedYes
Event16th Annual Neural Information Processing Systems Conference, NIPS 2002 - Vancouver, BC, Canada
Duration: Dec 9 2002Dec 14 2002

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other16th Annual Neural Information Processing Systems Conference, NIPS 2002
CountryCanada
CityVancouver, BC
Period12/9/0212/14/02

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

Wu, B., Wong, K. Y. M., & Bodoff, D. (2003). Mean-field approach to a probabilistic model in information retrieval. In Advances in Neural Information Processing Systems 15 - Proceedings of the 2002 Conference, NIPS 2002 (Advances in Neural Information Processing Systems). Neural information processing systems foundation.