Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation-Reference-Cited by-同舟云学术

Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation

Published:2024-03-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 17th ACM International Conference on Web Search and Data Mining
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Author:

Wang Shuyao¹^ORCID,Sui Yongduo¹^ORCID,Wu Jiancan²^ORCID,Zheng Zhi¹^ORCID,Xiong Hui³^ORCID

Affiliation:

1. School of Data Science, University of Science and Technology of China, Hefei, China

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

3. The Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou) & The Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Guangzhou, China

Funder

Guangdong OPPO Mobile Telecommunications Co., Ltd

The National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3616855.3635780

Reference62 articles.

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2. Gang Chen Jiawei Chen Fuli Feng Sheng Zhou and Xiangnan He. 2023. Unbiased Knowledge Distillation for Recommendation. In WSDM. 976--984.

3. Liyi Chen, Zhi Li, Weidong He, Gong Cheng, Tong Xu, Nicholas Jing Yuan, and Enhong Chen. 2022a. Entity Summarization via Exploiting Description Complementarity and Salience. IEEE TNNLS (2022).

4. Liyi Chen Zhi Li Yijun Wang Tong Xu Zhefeng Wang and Enhong Chen. 2020a. MMEA: entity alignment for multi-modal knowledge graph. In KSEM.

5. Liyi Chen, Zhi Li, Tong Xu, Han Wu, Zhefeng Wang, Nicholas Jing Yuan, and Enhong Chen. 2022b. Multi-modal Siamese Network for Entity Alignment. In SIGKDD.

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