Relevance Assessment
Relevance Assessment is a methodological process in information retrieval evaluation that determines the degree to which retrieved documents satisfy the informational needs expressed in a query, distinguished by its reliance on human or automated judgments to classify document-query pairs as relevant or non-relevant.
842
Publications
60K
Citations
1.8K
Authors
725
Institutions
Publications per year
2017–2026
152
842
Relevance feedback in information retrieval
J. J. Rocchio · Medical Entomology and Zoology · 1971 · 2.6K citations
Zhe Cao, Tao Qin, Tie‐Yan Liu et al. · 2007 · 2K citations
Predictive Analytics in Information Systems Research1
Galit Shmueli, Koppius · MIS Quarterly · 2011 · 1.2K citations · Full text
1–5 of 842
1.8K
Microsoft Research (United Kingdom), United Kingdom
15 publications · 4.1K citations
15
h-index
13
h-index
11
h-index
11
h-index
9
h-index
1–5 of 1.8K
725
Microsoft (United States)
Redmond, United States
22 h-index
70 publications
6.7K citations

University of Massachusetts Amherst
Amherst Center, United States
17 h-index
29 publications
1.7K citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
15 h-index
31 publications
2.2K citations

University of Glasgow
Glasgow, United Kingdom
15 h-index
35 publications
1.5K citations
University of Udine
Udine, Italy
14 h-index
25 publications
1.9K citations

Tampere University
Tampere, Finland
14 h-index
27 publications
7.4K citations
University of Maryland, College Park
College Park, United States
13 h-index
23 publications
2.4K citations

University of Waterloo
Waterloo, Canada
13 h-index
36 publications
905 citations
1–8 of 725
JASIS · 38 publications · 5.9K citations
31
h-index
JASIST · 36 publications · 2.5K citations
24
h-index
29 publications · 2.6K citations
20
h-index
22 publications · 2.5K citations
17
h-index
11 publications · 2K citations
11
h-index
1–5