Iterative Relevance Feedback
Iterative Relevance Feedback is a query refinement method in information retrieval that systematically incorporates user feedback across multiple cycles to progressively improve retrieval accuracy by adjusting the query representation based on relevance judgments of previously retrieved documents.
31
Publications
4K
Citations
111
Authors
55
Institutions
Publications per year
2017–2026
4
Parents
31
1–5 of 31
111
3
h-index
3
h-index
3
h-index
2
h-index
2
h-index
1–5 of 111
55

Open Text (Canada)
Waterloo, Canada
2 h-index
2 publications
128 citations
Idiap Research Institute
Martigny-Combe, Switzerland
2 h-index
4 publications
60 citations
University of Maryland, College Park
College Park, United States
2 h-index
4 publications
256 citations
University of California, Riverside
Riverside, United States
2 h-index
4 publications
116 citations

University of Bristol
Bristol, United Kingdom
1 h-index
3 publications
249 citations

University of Waterloo
Waterloo, Canada
1 h-index
6 publications
204 citations
IBM Research - India
New Delhi, India
1 h-index
2 publications
48 citations
AT&T (United States)
Bedminster, United States
1 h-index
1 publications
56 citations
1–8 of 55
TKDE · 2 publications · 206 citations
2
h-index
Repository · 1 publications · 11 citations
1
h-index
1 publications · 411 citations
1
h-index
JASIS · 1 publications · 1.5K citations
1
h-index
1 publications · 48 citations
1
h-index
1–5