JOURNAL ARTICLE

Implicit feedback for interactive information retrieval

Ryen W. White

Year: 2005 Journal:   ACM SIGIR Forum Vol: 39 (1)Pages: 70-70   Publisher: Association for Computing Machinery

Abstract

Searchers can find the construction of query statements for submission to Information Retrieval (IR) systems a problematic activity. These problems are confounded by uncertainty about the information they are searching for, or an unfamiliarity with the retrieval system being used or collection being searched. On the World Wide Web these problems are potentially more acute as searchers receive little or no training in how to search effectively. Relevance feedback (RF) techniques allow searchers to directly communicate what information is relevant and help them construct improved query statements. However, the techniques require explicit relevance assessments that intrude on searchers' primary lines of activity and as such, searchers may be unwilling to provide this feedback. Implicit feedback systems are unobtrusive and make inferences of what is relevant based on searcher interaction. They gather information to better represent searcher needs whilst minimising the burden of explicitly reformulating queries or directly providing relevance information.

Keywords:
Computer science Relevance (law) Information retrieval Relevance feedback Construct (python library) Human–computer information retrieval Cognitive models of information retrieval Query expansion Information needs World Wide Web Search engine Artificial intelligence Image retrieval

Metrics

43
Cited By
10.54
FWCI (Field Weighted Citation Impact)
221
Refs
0.98
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Information Retrieval and Search Behavior
Physical Sciences →  Computer Science →  Information Systems
Expert finding and Q&A systems
Physical Sciences →  Computer Science →  Information Systems
Topic Modeling
Physical Sciences →  Computer Science →  Artificial Intelligence

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