Improving document representations using relevance feedback: The RFA algorithm

Razvan Stefan Bot, Yi Fang Brook Wu

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

5 Scopus citations

Abstract

In this paper we present a document representation improvement technique, named the Relevance Feedback Accumulation (RFA) algorithm. Using prior relevance feedback assessments and a data mining measure called "support", the algorithm's learning function gradually improves document representations, over time and across users. Results show that the modified document representations yield lower dimensionality while improving retrieval effectiveness. The algorithm is efficient and scalable, suited for retrieval systems managing large document collections.

Original languageEnglish (US)
Title of host publicationCIKM 2004
Subtitle of host publicationProceedings of the Thirteenth ACM Conference on Information and Knowledge Management
EditorsD.A. Evans, L. Gravano, O. Herzog, C. Zhai, M. Ronthaler
Pages270-278
Number of pages9
StatePublished - Dec 1 2004
EventCIKM 2004: Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management - Washington, DC, United States
Duration: Nov 8 2004Nov 13 2004

Other

OtherCIKM 2004: Proceedings of the Thirteenth ACM Conference on Information and Knowledge Management
CountryUnited States
CityWashington, DC
Period11/8/0411/13/04

All Science Journal Classification (ASJC) codes

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

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