Probabilistic document-context based relevance feedback with limited relevance judgments

H. C. Wu, Wing Pong Robert Luk, K. F. Wong, K. L. Kwok

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

This paper presents our novel relevance feedback (RF) algorithm that uses the probabilistic document-context based retrieval model with limited relevance judgments for document re-ranking. Probabilities of the document-context based retrieval model are estimated from the top N (=20) documents in the initial retrieval. We use document-context based cosine similarity measure to find similar data for better probability estimation in order to reduce the data scarcity problem and the negative weighting problem. Our RF algorithm is promising because its mean average precision is statistically significantly better than the baseline using TREC-6 and TREC-7 data collections.
Original languageEnglish
Title of host publicationProceedings of the 15th ACM Conference on Information and Knowledge Management, CIKM 2006
Pages854-855
Number of pages2
DOIs
Publication statusPublished - 1 Dec 2006
Event15th ACM Conference on Information and Knowledge Management, CIKM 2006 - Arlington, VA, United States
Duration: 6 Nov 200611 Nov 2006

Conference

Conference15th ACM Conference on Information and Knowledge Management, CIKM 2006
CountryUnited States
CityArlington, VA
Period6/11/0611/11/06

Keywords

  • Document-context
  • Model

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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