Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation

Author:

Luo Sichun1,Xiao Yuanzhang2,Song Linqi1

Affiliation:

1. City University of Hong Kong & City University of Hong Kong Shenzhen Research Institute, Hong Kong, China

2. University of Hawaii at Manoa, Honolulu, HI, USA

Funder

Technological Breakthrough Project of Science, Technology and Innovation Commission of Shenzhen Municipality

Tencent AI Lab Rhino-Bird Gift Fund

Changsha Science and Technology Program International and Regional Science and Technology Cooperation Project

Hong Kong RGC

Hong Kong UGC Special Virtual Teaching and Learning (VTL)

InnoHK initiative, the Government of the HKSAR, Laboratory for AI-Powered Financial Technologies

Publisher

ACM

Reference19 articles.

1. A Federated Learning Approach for Privacy Protection in Context-Aware Recommender Systems

2. Muhammad Ammad-Ud-Din , Elena Ivannikova , Suleiman A Khan , Were Oyomno , Qiang Fu , Kuan Eeik Tan, and Adrian Flanagan . 2019 . Federated collaborative filtering for privacy-preserving personalized recommendation system. arXiv preprint arXiv:1901.09888 (2019). Muhammad Ammad-Ud-Din, Elena Ivannikova, Suleiman A Khan, Were Oyomno, Qiang Fu, Kuan Eeik Tan, and Adrian Flanagan. 2019. Federated collaborative filtering for privacy-preserving personalized recommendation system. arXiv preprint arXiv:1901.09888 (2019).

3. Secure Federated Matrix Factorization

4. Item-based top- N recommendation algorithms

5. Federated matrix factorization for privacy-preserving recommender systems

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