A two-stage mixture model for pseudo feedback

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

Abstract

Pseudo feedback is a commonly used technique to improve information retrieval performance. It assumes a few top-ranked documents to be relevant, and learns from them to improve the retrieval accuracy. A serious problem is that the performance is often very sensitive to the number of pseudo feedback documents. In this poster, we address this problem in a language modeling framework. We propose a novel two-stage mixture model, which is less sensitive to the number of pseudo feedback documents than an effective existing feedback model. The new model can tolerate a more flexible setting of the number of pseudo feedback documents without the danger of losing much retrieval accuracy.

Original languageEnglish (US)
Title of host publicationProceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery
Pages486-487
Number of pages2
ISBN (Print)1581138814, 9781581138818
DOIs
StatePublished - 2004
EventProceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - Sheffield, United Kingdom
Duration: Jul 25 2004Jul 29 2004

Publication series

NameProceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

Other

OtherProceedings of Sheffield SIGIR - Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
CountryUnited Kingdom
CitySheffield
Period7/25/047/29/04

Keywords

  • Information retrieval
  • Mixture model
  • Pseudo feedback

ASJC Scopus subject areas

  • Engineering(all)

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