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

Authors

32

  1. KS
    Kyung Soon Lee

    Jeonbuk National University, South Korea

    2 publications · 184 citations

    2

    h-index

  2. WB
    W. Bruce Croft

    University of Massachusetts Amherst, United States

    2 publications · 184 citations

    2

    h-index

  3. TS
    Tetsuya Sakai

    Waseda University, Japan

    2 publications · 90 citations

    2

    h-index

  4. SR
    Stephen Robertson

    Microsoft Research (United Kingdom), United Kingdom

    2 publications · 451 citations

    2

    h-index

  5. BH
    Ben He

    University of Chinese Academy of Sciences, China

    1 publications · 20 citations

    1

    h-index

1–5 of 32

Venues

  1. Information Processing & Management

    2 publications · 39 citations

    2

    h-index

  2. ACM Transactions on Asian Language Information Processing

    TALIP · 1 publications · 73 citations

    1

    h-index

1–2