Sequence-to-sequence Recommendation
Sequence-to-Sequence Recommendation is a sequence modeling framework that generates personalized recommendations by mapping an input sequence of user-item interactions to an output sequence of future items through an encoder-decoder architecture, distinguishing it from pointwise or session-based methods by explicitly modeling sequential dependencies in both input and output.
129
Publications
9.8K
Citations
524
Authors
157
Institutions
Publications per year
2017–2026
121
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129
A Simple Convolutional Generative Network for Next Item Recommendation
Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis et al. · 2019 · 625 citations · Full text
Intent Contrastive Learning for Sequential Recommendation
Yongjun Chen, Zhiwei Liu, Jia Li et al. · Proceedings of the ACM Web Conference 2022 · 2022 · 374 citations · Full text
Learning Fashion Compatibility with Bidirectional LSTMs
2017 · 310 citations · Full text
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Tsinghua University
Beijing, China
8 h-index
19 publications
1.3K citations
National University of Singapore
Singapore, Singapore
7 h-index
16 publications
1.2K citations
University of Technology Sydney
Sydney, Australia
6 h-index
8 publications
421 citations
University of California San Diego
San Diego, United States
4 h-index
10 publications
1.1K citations

The University of Queensland
Brisbane, Australia
4 h-index
11 publications
586 citations
Wuhan University
Wuhan, China
4 h-index
9 publications
538 citations
Nanyang Technological University
Singapore, Singapore
3 h-index
6 publications
159 citations

Peking University
Beijing, China
3 h-index
13 publications
1.3K citations
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Repository · 10 publications · 463 citations
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5 publications · 114 citations
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5 publications · 144 citations
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4 publications · 94 citations
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3 publications · 73 citations
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