Making mind and machine meet: A study of combining cognitive and algorithmic relevance feedback

Chirag Shah, Diane Kelly, Xin Fu

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

1 Scopus citations

Abstract

Using Saracevic's relevance types, we explore approaches to combining algorithm and cognitive relevance in a term relevance feedback scenario. Data collected from 21 users who provided relevance feedback about terms suggested by a system for 50 TREC HARD topics are used. The former type of feedback is considered as cognitive relevance and the latter type is considered as algorithm relevance. We construct retrieval runs using these two types of relevance feedback and experiment with ways of combining them with simple Boolean operators. Results show minimal differences in performance with respect to the different techniques.

Original languageEnglish (US)
Title of host publicationProceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'07
Pages877-878
Number of pages2
DOIs
StatePublished - 2007
Externally publishedYes
Event30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'07 - Amsterdam, Netherlands
Duration: Jul 23 2007Jul 27 2007

Publication series

NameProceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'07

Other

Other30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'07
Country/TerritoryNetherlands
CityAmsterdam
Period7/23/077/27/07

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Software
  • Applied Mathematics

Keywords

  • Cognitive & algorithmic relevance
  • Relevance feedback

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