Similarity Learning

Similarity Learning is a machine learning paradigm within content-based recommendation and metric learning that specifically learns a similarity function in vector space retrieval by inducing a distance metric that captures semantic relatedness between items based on their feature representations.

1.5K

Publications

101.7K

Citations

4.3K

Authors

1.2K

Institutions

Publications per year

2017–2026

719

Authors

4.3K

  1. SC
    Steven C. H. Hoi

    Nanyang Technological University, Singapore

    10 publications · 1K citations

    10

    h-index

  2. MW
    Meng Wang

    Hefei University of Technology, China

    10 publications · 958 citations

    10

    h-index

  3. WL
    Wei Liu

    Tencent (China), China

    9 publications · 1.1K citations

    9

    h-index

  4. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    9 publications · 484 citations

    9

    h-index

  5. XH
    Xian‐Sheng Hua

    Microsoft Research Asia (China), China

    9 publications · 838 citations

    9

    h-index

1–5 of 4.3K

Venues

  1. arXiv (Cornell University)

    Repository · 81 publications · 4.2K citations

    31

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 32 publications · 4.6K citations

    23

    h-index

  3. IEEE Access

    30 publications · 1.1K citations

    21

    h-index

  4. IEEE Transactions on Image Processing

    23 publications · 1.5K citations

    20

    h-index

  5. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 22 publications · 4.8K citations

    20

    h-index

1–5