Semantic-Guided Feature Distillation for Multimodal Recommendation-Reference-Cited by-同舟云学术

Semantic-Guided Feature Distillation for Multimodal Recommendation

Published:2023-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Multimedia
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Author:

Liu Fan¹^ORCID,Chen Huilin²^ORCID,Cheng Zhiyong³^ORCID,Nie Liqiang⁴^ORCID,Kankanhalli Mohan¹^ORCID

Affiliation:

1. National University of Singapore, Singapore, Singapore

2. Hefei University of Technology, Hefei, China

3. Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

4. Harbin Institute of Technology, Shenzhen, Shenzhen, China

Funder

the Shandong Project towards the Integration of Education and Industry

the National Research Foundation, Singapore under its Strategic Capability Research Centres Funding Initiative

the National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3611886

Reference51 articles.

1. Sungsoo Ahn , Shell Xu Hu , Andreas C. Damianou, Neil D. Lawrence , and Zhenwen Dai. 2019 . Variational Information Distillation for Knowledge Transfer. CVPRs ( 2019), 9155--9163. Sungsoo Ahn, Shell Xu Hu, Andreas C. Damianou, Neil D. Lawrence, and Zhenwen Dai. 2019. Variational Information Distillation for Knowledge Transfer. CVPRs (2019), 9155--9163.

2. Lessons from the Netflix prize challenge

3. Rose Catherine and William Cohen. 2017. TransNets: Learning to Transform for Recommendation. In RecSys. ACM 288--296. Rose Catherine and William Cohen. 2017. TransNets: Learning to Transform for Recommendation. In RecSys. ACM 288--296.

4. Chong Chen Min Zhang Yiqun Liu and Shaoping Ma. 2018a. Neural attentional rating regression with review-level explanations. In WWWC. IW3C2 1583--1592. Chong Chen Min Zhang Yiqun Liu and Shaoping Ma. 2018a. Neural attentional rating regression with review-level explanations. In WWWC. IW3C2 1583--1592.

5. Jingwu Chen Fuzhen Zhuang Xin Hong Xiang Ao Xing Xie and Qing He. 2018b. Attention-driven Factor Model for Explainable Personalized Recommendation. In SIGIR. ACM 909--912. Jingwu Chen Fuzhen Zhuang Xin Hong Xiang Ao Xing Xie and Qing He. 2018b. Attention-driven Factor Model for Explainable Personalized Recommendation. In SIGIR. ACM 909--912.

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