Session-based Content Recommendation
Session-Based Content Recommendation is a type of content-based recommendation approach that generates personalized content suggestions within a specific user session by modeling sequential interactions and contextual dependencies without relying on long-term user profiles.
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2.2K
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222
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94
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2017–2026
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Trends in content-based recommendation
Pasquale Lops, Dietmar Jannach, Cataldo Musto et al. · User Modeling and User-Adapted Interaction · 2019 · 197 citations · Full text
Neural Memory Streaming Recommender Networks with Adversarial Training
Qinyong Wang, Hongzhi Yin, Zhiting Hu et al. · 2018 · 170 citations
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Macquarie University
Sydney, Australia
5 h-index
13 publications
852 citations
University of Klagenfurt
Klagenfurt, Austria
4 h-index
4 publications
582 citations

Huazhong University of Science and Technology
Wuhan, China
3 h-index
8 publications
168 citations
Wuhan University
Wuhan, China
3 h-index
3 publications
130 citations
TU Dortmund University
Dortmund, Germany
3 h-index
5 publications
404 citations

Taiyuan University of Technology
Taiyuan, China
3 h-index
6 publications
215 citations
Beijing Institute of Big Data Research
Beijing, China
2 h-index
2 publications
78 citations

Jinan University
Guangzhou, China
2 h-index
6 publications
104 citations
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9 publications · 220 citations
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7 publications · 190 citations
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6 publications · 264 citations
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4 publications · 588 citations
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3 publications · 62 citations
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