Autoencoder-based Recommendation

Autoencoder-Based Recommendation is a recommendation method that employs autoencoder architectures to model user-item interactions by reconstructing observed preference data, thereby capturing latent representations distinct from other collaborative filtering approaches through nonlinear dimensionality reduction.

150

Publications

9.6K

Citations

514

Authors

218

Institutions

Publications per year

2017–2026

140

Authors

514

  1. LY
    Lina Yao

    UNSW Sydney, Australia

    3 publications · 780 citations

    3

    h-index

  2. HY
    Haiping Yu

    Wuhan University, China

    3 publications · 262 citations

    3

    h-index

  3. XL
    Xue Liu

    McGill University, Canada

    3 publications · 193 citations

    3

    h-index

  4. YP
    Yiteng Pan

    Wuhan University, China

    3 publications · 262 citations

    3

    h-index

  5. CM
    Chen Ma

    McGill University, Canada

    3 publications · 193 citations

    3

    h-index

1–5 of 514

Venues

  1. IEEE Access

    16 publications · 451 citations

    14

    h-index

  2. arXiv (Cornell University)

    Repository · 10 publications · 958 citations

    10

    h-index

  3. Knowledge-Based Systems

    7 publications · 238 citations

    7

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 5 publications · 557 citations

    5

    h-index

  5. Expert Systems with Applications

    5 publications · 167 citations

    5

    h-index

1–5