Distributionally Robust Graph-based Recommendation System
Author:
Affiliation:
1. The State Key Laboratory of Blockchain and Data Security, Zhejiang University, Hangzhou, China
2. Zhejiang University, Hangzhou, China
3. Hangzhou City University, Hangzhou, China
4. Intelligence Indeed, Hangzhou, China
Funder
Supercomputing Center of Hangzhou City University
National Natural Science Foundation of China
Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3589334.3645598
Reference60 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. arXiv preprint arXiv:2302.08191 (2023).
3. Collaborative Similarity Embedding for Recommender Systems
4. AutoDebias: Learning to Debias for Recommendation
5. b. Bias and debias in recommender system: A survey and future directions;Chen Jiawei;ACM Transactions on Information Systems,2023
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