Robust Sequential Recommendation

Robust Sequential Recommendation is a sequential recommendation framework that enhances predictive reliability under noisy, sparse, or adversarial user behavior by explicitly modeling uncertainties and distributional shifts in user interaction sequences.

6

Publications

256

Citations

18

Authors

11

Institutions

Publications per year

2017–2026

6

Authors

18

  1. HZ
    Hai-Tao Zheng

    University of California, Santa Barbara, United States

    1 publications · 52 citations

    1

    h-index

  2. JW
    Jinpeng Wang

    Renmin University of China, China

    1 publications · 52 citations

    1

    h-index

  3. ZZ
    Ziyun Zeng

    Tsinghua University, China

    1 publications · 52 citations

    1

    h-index

  4. ZF
    Ziwei Fan

    University of Illinois Chicago, United States

    1 publications · 22 citations

    1

    h-index

  5. XL
    Xingyu Lu

    University of Chicago, United States

    1 publications · 52 citations

    1

    h-index

1–5 of 18

Venues

  1. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 29 citations

    1

    h-index

  2. Neural Networks

    1 publications · 15 citations

  3. Applied Intelligence

    1 publications · 10 citations

1–3