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
Parents
6
Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions
Wujiang Xu, Qitian Wu, Runzhong Wang et al. · 2024 · 29 citations · Full text
Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation
Ziwei Fan, Zhiwei Liu, Hao Peng et al. · 2023 · 22 citations
1–5 of 6
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1–5 of 18
11

Zhejiang University
Hangzhou, China
1 h-index
4 publications
452 citations
University of Illinois Chicago
Chicago, United States
1 h-index
2 publications
44 citations
Naver (South Korea)
Seongnam-si, South Korea
1 h-index
1 publications
29 citations
Brazosport Eye Institute
Lake Jackson, United States
1 h-index
1 publications
22 citations
National University of Singapore
Singapore, Singapore
1 h-index
1 publications
113 citations
Korea Advanced Institute of Science and Technology
Daejeon, South Korea
1 h-index
3 publications
87 citations
Huawei Technologies (China)
Shenzhen, China
1 h-index
1 publications
52 citations
Tsinghua University
Beijing, China
1 h-index
8 publications
416 citations
1–8 of 11
Conference · 1 publications · 29 citations
1
h-index
1 publications · 15 citations
1 publications · 10 citations
1–3