Zero-shot Recommendation
Zero-Shot Recommendation is a recommendation paradigm that enables the prediction of user preferences for items or domains without any prior interaction data by leveraging semantic embeddings and cross-modal transfer from auxiliary information.
17
Publications
865
Citations
78
Authors
30
Institutions
Publications per year
2017–2026
16
Variants
Zero-shot Recommender Systems
Parents
17
From Zero-Shot Learning to Cold-Start Recommendation
Jingjing Li, Mengmeng Jing, Ke Lü et al. · Proceedings of the AAAI Conference on Artificial Intelligence · 2019 · 153 citations · Full text
Large Language Models as Zero-Shot Conversational Recommenders
Zhankui He, Zhouhang Xie, Rahul Jha et al. · 2023 · 118 citations · Full text
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30

Cornell University
Ithaca, United States
1 h-index
1 publications
118 citations
Singapore Management University
Singapore, Singapore
1 h-index
5 publications
155 citations

Beihang University
Beijing, China
1 h-index
1 publications
45 citations

Hanyang University
Seoul, South Korea
1 h-index
3 publications
162 citations
Virginia Tech
Blacksburg, United States
1 h-index
1 publications
61 citations
Guangdong University of Technology
Guangzhou, China
1 h-index
1 publications
19 citations
TCL (China)
Huizhou, China
1 h-index
1 publications
19 citations
Tianjin University
Tianjin, China
1 h-index
4 publications
140 citations
1–8 of 30
Repository · 6 publications · 167 citations
6
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TVCG · 1 publications · 35 citations
1
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Conference · 1 publications · 153 citations
1
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1 publications · 31 citations
1
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1 publications · 19 citations
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