Query-specific clustering of search results based on document-context similarity scores

E. K.F. Dang, Wing Pong Robert Luk, D. L. Lee, K. S. Ho, S. C.F. Chan

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

1 Citation (Scopus)

Abstract

This paper presents a pilot study of query-specific clustering that uses our novel document-context based similarity scores as compared with document similarity scores. Clustering is applied to the top 1000 retrieved documents for a given query. Clustering effectiveness is evaluated based on the MK1 score for TREC-2, TREC-6 and TREC-7 test collections. Encouraging results were obtained whereby document-context clustering produces better MK1 scores than document clustering with a 95% confidence level if precision and recall are equally important.
Original languageEnglish
Title of host publicationProceedings of the 15th ACM Conference on Information and Knowledge Management, CIKM 2006
Pages886-887
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
Country/TerritoryUnited States
CityArlington, VA
Period6/11/0611/11/06

Keywords

  • Context-based model
  • Document clustering
  • Experimentations

ASJC Scopus subject areas

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

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