Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering-Reference-Cited by-同舟云学术

Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering

Published:2024-08-24 Issue: Volume:34 Page:3425-3436
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
language:
Short-container-title:

Author:

Wu Yihong¹^ORCID,Zhang Le²^ORCID,Mo Fengran¹^ORCID,Zhu Tianyu³^ORCID,Ma Weizhi⁴^ORCID,Nie Jian-Yun¹^ORCID

Affiliation:

1. Université de Montréal, Montréal, Canada

2. Mila - Quebec AI Institute, Montréal, Canada

3. MIIT Key Laboratory of Data Intelligence and Management, Beihang University, Beijing, China

4. Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671840

Reference50 articles.

1. David H Ackley, Geoffrey E Hinton, and Terrence J Sejnowski. 1985. A learning algorithm for Boltzmann machines. Cognitive science, Vol. 9, 1 (1985), 147--169.

2. Xuheng Cai, Chao Huang, Lianghao Xia, and Xubin Ren. 2023. LightGCL: Simple yet effective graph contrastive learning for recommendation. arXiv preprint arXiv:2302.08191 (2023).

3. Jiawei Chen, Junkang Wu, Jiancan Wu, Sheng Zhou, Xuezhi Cao, and Xiangnan He. 2023. Adap-tau: Adaptively Modulating Embedding Magnitude for Recommendation. arXiv preprint arXiv:2302.04775 (2023).

4. Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597--1607.

5. Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021. Simcse: Simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821 (2021).