Pseudo-relevance Feedback

Pseudo-Relevance Feedback is a query expansion technique in information retrieval where the top-ranked documents from an initial retrieval are automatically assumed to be relevant and used to augment the original query without explicit user relevance judgments.

214

Publications

10.1K

Citations

510

Authors

204

Institutions

Publications per year

2017–2026

51

Authors

510

  1. WB
    W. Bruce Croft

    University of Massachusetts Amherst, United States

    12 publications · 1K citations

    12

    h-index

  2. JX
    Jimmy Xiangji Huang

    York University, Canada

    10 publications · 312 citations

    10

    h-index

  3. JS
    Jagendra Singh

    Bennett University, India

    7 publications · 319 citations

    7

    h-index

  4. HL
    Hung-yi Lee

    National Taiwan University, Taiwan

    6 publications · 110 citations

    6

    h-index

  5. LL
    Lin-shan Lee

    National Taiwan University, Taiwan

    6 publications · 99 citations

    6

    h-index

1–5 of 510

Venues

  1. Text REtrieval Conference

    Conference · 6 publications · 81 citations

    6

    h-index

  2. 6

    h-index

  3. Journal of Information Science

    JIS · 5 publications · 79 citations

    5

    h-index

  4. Information Processing & Management

    5 publications · 161 citations

    5

    h-index

  5. ACM Transactions on Information Systems

    4 publications · 119 citations

    4

    h-index

1–5