GPFedRec: Graph-Guided Personalization for Federated Recommendation-Reference-Cited by-同舟云学术

GPFedRec: Graph-Guided Personalization for Federated Recommendation

Published:2024-08-24 Issue: Volume:13 Page:4131-4142
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Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Zhang Chunxu¹^ORCID,Long Guodong²^ORCID,Zhou Tianyi³^ORCID,Zhang Zijian⁴^ORCID,Yan Peng²^ORCID,Yang Bo¹^ORCID

Affiliation:

1. College of Computer Science and Technology, Jilin University & Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, China

2. Australian Artificial Intelligence Institute, FEIT, University of Technology Sydney, Sydney, Australia

3. Computer Science and UMIACS, University of Maryland, Maryland, USA

4. College of Computer Science and Technology, Jilin University & City University of Hong Kong, Changchun, Jilin, China

Funder

the Key Science and Technology Development Plan of Jilin Province

the National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671702

Reference51 articles.

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3. Second workshop on information heterogeneity and fusion in recommender systems (HetRec2011)

4. Secure Federated Matrix Factorization

5. Jie Chen, Tengfei Ma, and Cao Xiao. 2018. FastGCN: Fast learning with graph convolutional networks via importance sampling. In International Conference on Learning Representations. International Conference on Learning Representations, ICLR.