Deep Learning For Recommendation

Deep Learning for Recommendation is a methodological framework within recommendation systems that leverages deep neural architectures to model complex, non-linear user-item interactions by automatically extracting latent features from high-dimensional, heterogeneous data sources.

1.4K

Publications

118.8K

Citations

4.7K

Authors

1.1K

Institutions

Publications per year

2017–2026

1.3K

Authors

4.7K

  1. XH
    Xiangnan He

    University of Science and Technology of China, China

    21 publications · 13.8K citations

    19

    h-index

  2. HY
    Hongzhi Yin

    The University of Queensland, Australia

    20 publications · 2.7K citations

    17

    h-index

  3. RT
    Ruiming Tang

    Huawei Technologies (China), China

    16 publications · 3.8K citations

    16

    h-index

  4. XZ
    Xiangyu Zhao

    City University of Hong Kong, Hong Kong SAR China

    17 publications · 1.9K citations

    16

    h-index

  5. XH
    Xiuqiang He

    Huawei Technologies (China), China

    16 publications · 4.1K citations

    16

    h-index

1–5 of 4.7K

Venues

  1. arXiv (Cornell University)

    Repository · 96 publications · 7K citations

    34

    h-index

  2. Expert Systems with Applications

    55 publications · 2.8K citations

    27

    h-index

  3. Knowledge-Based Systems

    40 publications · 2.4K citations

    26

    h-index

  4. IEEE Access

    58 publications · 2.4K citations

    25

    h-index

  5. Information Sciences

    32 publications · 1.6K citations

    24

    h-index

1–5