Online Sequential Recommendation
Online Sequential Recommendation is a sequential recommendation paradigm that operates in real-time by incrementally updating user representations and generating recommendations from streaming user-item interaction data without reprocessing the entire historical sequence.
55
Publications
2.3K
Citations
215
Authors
67
Institutions
Publications per year
2017–2026
48
Variants
Streaming Sequential Recommendation
Parents
55
Fuyu Lv, Taiwei Jin, Changlong Yu et al. · 2019 · 148 citations
User Preference Learning for Online Social Recommendation
Zhou Zhao, Hanqing Lu, Deng Cai et al. · IEEE Transactions on Knowledge and Data Engineering · 2016 · 118 citations
Long- and short-term self-attention network for sequential recommendation
Chengfeng Xu, Jian Feng, Pengpeng Zhao et al. · Neurocomputing · 2020 · 113 citations
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67

Alibaba Group (China)
Hangzhou, China
5 h-index
18 publications
1.2K citations

Zhejiang University
Hangzhou, China
5 h-index
13 publications
865 citations
Tsinghua University
Beijing, China
4 h-index
9 publications
373 citations
Wuhan University
Wuhan, China
4 h-index
6 publications
338 citations

Renmin University of China
Beijing, China
3 h-index
10 publications
313 citations
University of Science and Technology of China
Hefei, China
3 h-index
10 publications
583 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
3 h-index
5 publications
666 citations

Soochow University
Suzhou, China
3 h-index
7 publications
475 citations
1–8 of 67
4 publications · 109 citations
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4 publications · 82 citations
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Repository · 3 publications · 138 citations
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3 publications · 61 citations
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TKDE · 2 publications · 154 citations
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