Multi-task Learning For Recommendation

Multi-Task Learning for Recommendation is a recommendation framework that simultaneously models multiple related recommendation tasks within a shared representation space to improve overall performance by leveraging commonalities and task-specific patterns across auxiliary objectives.

108

Publications

6.6K

Citations

461

Authors

139

Institutions

Publications per year

2017–2026

94

Authors

461

  1. TC
    Tat‐Seng Chua

    National University of Singapore, Singapore

    4 publications · 189 citations

    4

    h-index

  2. XL
    Xiu Li

    Tsinghua University, China

    3 publications · 116 citations

    3

    h-index

  3. CM
    Chang Meng

    Tsinghua University, China

    3 publications · 116 citations

    3

    h-index

  4. HW
    Haifeng Wang

    Baidu (China), China

    3 publications · 71 citations

    3

    h-index

  5. HW
    Hao Wu

    Yunnan University, China

    3 publications · 111 citations

    3

    h-index

1–5 of 461

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 98 citations

    5

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 5 publications · 372 citations

    5

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 379 citations

    4

    h-index

  4. Applied Intelligence

    3 publications · 55 citations

    3

    h-index

  5. ACM Transactions on Information Systems

    3 publications · 110 citations

    3

    h-index

1–5