Sequence-aware Recommendation
Sequence-Aware Recommendation is a type of recommender system approach that incorporates temporal or sequential patterns in user interactions to model the dynamic evolution of user preferences and context over time, distinguishing itself from static or context-independent recommendation methods by explicitly leveraging the order and structure of past user behavior.
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17.4K
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841
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273
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2017–2026
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What to Do Next: Modeling User Behaviors by Time-LSTM
Yu Zhu, Hao Li, Yikang Liao et al. · 2017 · 473 citations · Full text
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Alibaba Group (China)
Hangzhou, China
9 h-index
47 publications
21.8K citations

Zhejiang University
Hangzhou, China
7 h-index
24 publications
4.2K citations
Tsinghua University
Beijing, China
7 h-index
16 publications
1.2K citations
University of Technology Sydney
Sydney, Australia
7 h-index
11 publications
738 citations
Nanyang Technological University
Singapore, Singapore
7 h-index
20 publications
2.9K citations
Wuhan University
Wuhan, China
6 h-index
16 publications
524 citations

Peking University
Beijing, China
6 h-index
22 publications
1K citations
University of Science and Technology of China
Hefei, China
6 h-index
15 publications
1.8K citations
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Repository · 12 publications · 582 citations
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Conference · 6 publications · 572 citations
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