Listwise Ranking Models

Listwise Ranking Models is a type of learning-to-rank framework that treats ranking as a holistic optimization problem by directly modeling the permutation of an entire document list with respect to a query, thereby distinguishing itself from pointwise and pairwise approaches through its global consideration of list-level structure and relevance dependencies.

38

Publications

6.2K

Citations

128

Authors

50

Institutions

Publications per year

2017–2026

13

Variants

Listwise Learning-to-rank Models

Authors

128

  1. TL
    Tie‐Yan Liu

    Microsoft Research Asia (China), China

    5 publications · 4.3K citations

    5

    h-index

  2. YL
    Yanyan Lan

    Chinese Academy of Sciences, China

    4 publications · 151 citations

    4

    h-index

  3. SN
    Shuzi Niu

    Chinese Academy of Sciences, China

    3 publications · 95 citations

    3

    h-index

  4. JG
    Jiafeng Guo

    Chinese Academy of Sciences, China

    3 publications · 95 citations

    3

    h-index

  5. XC
    Xueqi Cheng

    Chinese Academy of Sciences, China

    3 publications · 95 citations

    3

    h-index

1–5 of 128

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 46 citations

    2

    h-index

  2. IEEE Transactions on Image Processing

    2 publications · 802 citations

    2

    h-index

  3. IEEE Access

    1 publications · 20 citations

    1

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 17 citations

    1

    h-index

  5. Journal of Computational Science

    1 publications · 14 citations

    1

    h-index

1–5