Pseudo-feedback Document Selection
Pseudo-Feedback Document Selection is a retrieval technique in information retrieval that automatically selects top-ranked documents from an initial search result to serve as pseudo-relevant feedback for query expansion, distinguishing itself by assuming relevance based on positional ranking without manual annotation.
11
Publications
1.1K
Citations
32
Authors
20
Institutions
Publications per year
2017–2026
2
Parents
11
Selecting good expansion terms for pseudo-relevance feedback
Guihong Cao, Jian‐Yun Nie, Jianfeng Gao et al. · 2008 · 434 citations
Positional relevance model for pseudo-relevance feedback
Yuanhua Lv, ChengXiang Zhai · 2010 · 214 citations
KLE at TREC 2008 Blog Track: Blog Post and Feed Retrieval
Yeha Lee, Seung‐Hoon Na, Jun-Gi Kim et al. · 2008 · 55 citations
1–5 of 11
32
2
h-index
2
h-index
2
h-index
2
h-index
1
h-index
1–5 of 32
20
Toshiba (Japan)
Tokyo, Japan
2 h-index
4 publications
236 citations
Jeonbuk National University
Jeonju, South Korea
2 h-index
2 publications
184 citations

University of Massachusetts Amherst
Amherst Center, United States
2 h-index
3 publications
349 citations
University of Cambridge
Cambridge, United Kingdom
1 h-index
1 publications
17 citations
University of Delaware
Newark, United States
1 h-index
2 publications
40 citations
University of Illinois Urbana-Champaign
Urbana, United States
1 h-index
2 publications
428 citations
Université de Montréal
Montreal, Canada
1 h-index
2 publications
868 citations
Institute of Software
Beijing, China
1 h-index
3 publications
60 citations
1–8 of 20
2 publications · 39 citations
2
h-index
TALIP · 1 publications · 73 citations
1
h-index
1–2