Adaptive Query Expansion

Adaptive Query Expansion is a query refinement technique in information retrieval that dynamically augments the original query with additional terms selected from relevant documents identified through user feedback, distinguishing itself by iteratively adjusting the expansion strategy based on relevance judgments to improve retrieval performance.

66

Publications

3.1K

Citations

198

Authors

89

Institutions

Publications per year

2017–2026

12

Authors

198

  1. RP
    Rajendra Pamula

    Indian Institute of Technology Dhanbad, India

    3 publications · 48 citations

    3

    h-index

  2. CJ
    Cornelis J. van Rijsbergen

    University of Glasgow, United Kingdom

    2 publications · 143 citations

    2

    h-index

  3. NR
    Naren Ramakrishnan

    Virginia Tech, United States

    2 publications · 74 citations

    2

    h-index

  4. CL
    Chang‐Tien Lu

    Virginia Tech, United States

    2 publications · 74 citations

    2

    h-index

  5. IK
    Ilyes Khennak

    University of Sciences and Technology Houari Boumediene, Algeria

    2 publications · 64 citations

    2

    h-index

1–5 of 198

Venues

  1. Information Processing & Management

    7 publications · 685 citations

    7

    h-index

  2. Text REtrieval Conference

    Conference · 3 publications · 80 citations

    3

    h-index

  3. ACM Transactions on Information Systems

    3 publications · 167 citations

    3

    h-index

  4. Applied Intelligence

    2 publications · 63 citations

    2

    h-index

  5. Information Retrieval

    2 publications · 77 citations

    2

    h-index

1–5