AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations-Reference-Cited by-同舟云学术

AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations

Published:2024-07-10 Issue: Volume:38 Page:1242-1252
ISSN:
Container-title:Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Wu Wei¹^ORCID,Wang Chao²^ORCID,Shen Dazhong³^ORCID,Qin Chuan⁴^ORCID,Chen Liyi¹^ORCID,Xiong Hui⁵^ORCID

Affiliation:

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

2. DLRC, Guangzhou HKUST Fok Ying Tung Research Institute & School of Computer Science and Technology, University of Science and Technology of China, Guangzhou, Guangdong, China

3. Shanghai Artificial Intelligence Laboratory, Shanghai, China

4. Career Science Lab, BOSS Zhipin & PBC School of Finance, Tsinghua University, Beijing, China

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

Funder

National Natural Science Foundation of China

Guangzhou-HKUST(GZ) Joint Funding Program

Project funded by China Postdoctoral Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626772.3657724

Reference54 articles.

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

2. Xuheng Cai, Chao Huang, Lianghao Xia, and Xubin Ren. 2023. LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation. In The Eleventh International Conference on Learning Representations. https://openreview.net/forum?id=FKXVK9dyMM

3. e. Efficient neural matrix factorization without sampling for recommendation;Chen Chong;ACM Transactions on Information Systems (TOIS),2020

4. Measuring and Relieving the Over-Smoothing Problem for Graph Neural Networks from the Topological View

5. Liyi Chen, Zhi Li, Weidong He, Gong Cheng, Tong Xu, Nicholas Jing Yuan, and Enhong Chen. 2023. Entity Summarization via Exploiting Description Complementarity and Salience. IEEE transactions on neural networks and learning systems, Vol. 34, 11 (2023), 8297--8309.