Zero-shot Recommendation

Zero-Shot Recommendation is a recommendation paradigm that enables the prediction of user preferences for items or domains without any prior interaction data by leveraging semantic embeddings and cross-modal transfer from auxiliary information.

17

Publications

865

Citations

78

Authors

30

Institutions

Publications per year

2017–2026

16

Variants

Zero-shot Recommender Systems

Authors

78

  1. RX
    Ruobing Xie

    Tencent (China), China

    2 publications · 68 citations

    2

    h-index

  2. JM
    Julian McAuley

    University of California San Diego, United States

    2 publications · 141 citations

    2

    h-index

  3. EL
    Ee‐Peng Lim

    Singapore Management University, Singapore

    2 publications · 53 citations

    2

    h-index

  4. SS
    Scott Sanner

    University of Toronto, Canada

    1 publications · 112 citations

    1

    h-index

  5. RJ
    Rahul Jha

    Cornell University, United States

    1 publications · 118 citations

    1

    h-index

1–5 of 78

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 167 citations

    6

    h-index

  2. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 1 publications · 35 citations

    1

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 153 citations

    1

    h-index

  4. ACM Transactions on Recommender Systems

    1 publications · 31 citations

    1

    h-index

  5. IEEE Transactions on Services Computing

    1 publications · 19 citations

    1

    h-index

1–5