Feedback-Based Ranking is a retrieval optimization process in information retrieval that distinguishes itself by iteratively refining document rankings through explicit or implicit user feedback on initially retrieved results.
146
Publications
8.9K
Citations
390
Authors
164
Institutions
Publications per year
2017–2026
25
Parents
Publications
146
Relevance feedback in information retrieval
J. J. Rocchio · Medical Entomology and Zoology · 1971 · 2.6K citations
Optimization of relevance feedback weights
Chris Buckley, Gerard Salton · 1995 · 185 citations
Oren Kurland, Lillian Lee · 2005 · 180 citations
1–5 of 146
Authors
390
- WB
6
h-index
- MD
6
h-index
- OK
5
h-index
- SW
5
h-index
- EB
4
h-index
1–5 of 390
Institutions
164
Cornell University
Ithaca, United States
10 h-index
16 publications
989 citations
Microsoft (United States)
Redmond, United States
10 h-index
12 publications
3.5K citations
University of Amsterdam
Amsterdam, The Netherlands
8 h-index
26 publications
1.8K citations
University of Massachusetts Amherst
Amherst Center, United States
8 h-index
15 publications
374 citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
6 h-index
12 publications
3.4K citations
Technion – Israel Institute of Technology
Haifa, Israel
6 h-index
9 publications
314 citations
Yahoo (United States)
Sunnyvale, United States
5 h-index
18 publications
494 citations
Amsterdam University of the Arts
Amsterdam, The Netherlands
5 h-index
16 publications
1.5K citations
1–8 of 164
Venues
- ACM Transactions on Information Systems
5 publications · 190 citations
5
h-index
- arXiv (Cornell University)
Repository · 4 publications · 82 citations
4
h-index
- Information Processing & Management
4 publications · 98 citations
4
h-index
- Information Sciences
3 publications · 116 citations
3
h-index
- Text REtrieval Conference
Conference · 3 publications · 60 citations
3
h-index
1–5