Challenges and Advancement in Federated Recommendation System: A Comprehensive Review
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-51167-7_22
Reference40 articles.
1. K. Shah, A. Salunke, S. Dongare, K. Antala, Recommender Systems: An Overview of Different Approaches to Recommendations (2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017). https://doi.org/10.1109/iciiecs.2017.8276172
2. Zhong, Z., & Li, Y. (2016). A recommender system for healthcare based on human-centric modelling. 2016 IEEE 13th International Conference on e-Business Engineering (ICEBE). https://doi.org/10.1109/icebe.2016.055
3. A. Friedman, B.P. Knijnenburg, K. Vanhecke, L. Martens, S. Berkovsky, Privacy aspects of Recommender Systems (Recommender Systems Handbook, 2015), pp. 649–688. https://doi.org/10.1007/978-1-4899-7637-6_19
4. J. Konečný, H.B. McMahan, D. Ramage, P. Richtárik, Federated optimization: Distributed machine learning for on-device intelligence (arXiv preprint arXiv:1610.02527, 2016)
5. K. Maeng, H. Lu, L. Melis, J. Nguyen, M. Rabbat, C.-J. Wu, Towards fair federated recommendation learning: Characterizing the inter-dependence of system and data heterogeneity. Sixteenth ACM Conference on Recommender Systems. (2022). https://doi.org/10.1145/3523227.3546759
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