How to Learn Item Representation for Cold-Start Multimedia Recommendation?-Reference-Cited by-同舟云学术

How to Learn Item Representation for Cold-Start Multimedia Recommendation?

Published:2020-10-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 28th ACM International Conference on Multimedia
language:
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Author:

Du Xiaoyu¹,Wang Xiang¹,He Xiangnan²,Li Zechao³,Tang Jinhui³,Chua Tat-Seng¹

Affiliation:

1. National University of Singapore, Singapore, Singapore

2. University of Science and Technology of China, Hefei, China

3. Nanjing University of Science and Technology, Nanjing, China

Funder

National Research Foundation of Singapore

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3394171.3413628

Reference51 articles.

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2. Cold-Start Recommendation with Provable Guarantees: A Decoupled Approach

3. Oren Barkan Noam Koenigstein Eylon Yogev and Ori Katz. 2019. CB2CF: a neural multiview content-to-collaborative filtering model for completely cold item recommendations. In RecSys. 228--236. Oren Barkan Noam Koenigstein Eylon Yogev and Ori Katz. 2019. CB2CF: a neural multiview content-to-collaborative filtering model for completely cold item recommendations. In RecSys. 228--236.

4. Rianne van den Berg Thomas N Kipf and Max Welling. 2017. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 (2017). Rianne van den Berg Thomas N Kipf and Max Welling. 2017. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 (2017).

5. Jingyuan Chen Hanwang Zhang Xiangnan He Liqiang Nie Wei Liu and Tat-Seng Chua. 2017. Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In SIGIR. 335--344. Jingyuan Chen Hanwang Zhang Xiangnan He Liqiang Nie Wei Liu and Tat-Seng Chua. 2017. Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In SIGIR. 335--344.

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