Shared Latent Space Learning

Shared Latent Space Learning is a cross-domain recommendation method that projects user and item representations from multiple domains into a common latent space, enabling knowledge transfer by aligning structural and semantic patterns across domains while preserving domain-specific characteristics.

147

Publications

11.4K

Citations

502

Authors

216

Institutions

Publications per year

2017–2026

113

Authors

502

  1. ML
    Mingsheng Long

    Tsinghua University, China

    5 publications · 391 citations

    5

    h-index

  2. AT
    Alexander Tuzhilin

    New York University, United States

    5 publications · 399 citations

    5

    h-index

  3. JW
    Jianmin Wang

    Tsinghua University, China

    4 publications · 334 citations

    4

    h-index

  4. HX
    Hui Xiong

    Rutgers, The State University of New Jersey, United States

    3 publications · 66 citations

    3

    h-index

  5. CW
    Chengde Wan

    ETH Zurich, Switzerland

    3 publications · 215 citations

    3

    h-index

1–5 of 502

Venues

  1. arXiv (Cornell University)

    Repository · 17 publications · 1.8K citations

    14

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 7 publications · 472 citations

    7

    h-index

  3. Knowledge-Based Systems

    6 publications · 166 citations

    6

    h-index

  4. IEEE Transactions on Image Processing

    5 publications · 401 citations

    5

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 5 publications · 461 citations

    5

    h-index

1–5