Deep Collaborative Filtering

Deep Collaborative Filtering is a machine learning approach within collaborative filtering that integrates deep neural networks to model complex user-item interactions by jointly learning latent representations from raw input features and interaction data.

285

Publications

27.7K

Citations

974

Authors

354

Institutions

Publications per year

2017–2026

262

Authors

974

  1. XH
    Xiangnan He

    University of Science and Technology of China, China

    10 publications · 7.9K citations

    10

    h-index

  2. HS
    Harald Steck

    Netflix (United States), United States

    4 publications · 763 citations

    4

    h-index

  3. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    4 publications · 393 citations

    4

    h-index

  4. YZ
    Yin Zheng

    Tsinghua University, China

    4 publications · 228 citations

    4

    h-index

  5. QL
    Qinglong Li

    Kyung Hee University, South Korea

    3 publications · 121 citations

    3

    h-index

1–5 of 974

Venues

  1. IEEE Access

    20 publications · 1.1K citations

    18

    h-index

  2. arXiv (Cornell University)

    Repository · 17 publications · 2.1K citations

    13

    h-index

  3. Expert Systems with Applications

    8 publications · 1.1K citations

    8

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 7 publications · 707 citations

    7

    h-index

  5. Applied Sciences

    6 publications · 311 citations

    6

    h-index

1–5