Query Suggestion Systems

Query Suggestion Systems is an information retrieval mechanism that dynamically proposes refined or alternative queries to users during search tasks, distinguished by its reliance on historical query logs, user behavior patterns, and contextual cues to enhance query formulation and retrieval effectiveness.

86

Publications

4K

Citations

272

Authors

106

Institutions

Publications per year

2017–2026

17

Variants

Query Suggestion System

Authors

272

  1. DK
    Diane Kelly

    Swansea University, United Kingdom

    3 publications · 245 citations

    3

    h-index

  2. HD
    Hongbo Deng

    Alibaba Group (China), China

    3 publications · 118 citations

    3

    h-index

  3. KT
    Katsumi Tanaka

    Kyoto University, Japan

    3 publications · 98 citations

    3

    h-index

  4. TS
    Tetsuya Sakai

    Waseda University, Japan

    3 publications · 110 citations

    3

    h-index

  5. GL
    Guoliang Li

    Tsinghua University, China

    3 publications · 79 citations

    3

    h-index

1–5 of 272

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 4 publications · 149 citations

    4

    h-index

  2. Information Processing & Management

    4 publications · 112 citations

    4

    h-index

  3. AfCS-Nature Molecule Pages

    1 publications · 11 citations

    1

    h-index

  4. International Journal of Artificial Intelligence Tools

    IJAIT · 1 publications · 13 citations

    1

    h-index

  5. 1

    h-index

1–5