A Survey on the use of Federated Learning in Privacy-Preserving Recommender Systems-Reference-Cited by-同舟云学术

A Survey on the use of Federated Learning in Privacy-Preserving Recommender Systems

Published:2024 Issue: Volume:5 Page:227-247
ISSN:2644-1268
Container-title:IEEE Open Journal of the Computer Society
language:
Short-container-title:IEEE Open J. Comput. Soc.

Author:

Chronis Christos¹^ORCID,Varlamis Iraklis¹^ORCID,Himeur Yassine²^ORCID,Sayed Aya N.³^ORCID,AL-Hasan Tamim M.²^ORCID,Nhlabatsi Armstrong³^ORCID,Bensaali Faycal²^ORCID,Dimitrakopoulos George¹^ORCID

Affiliation:

1. Department of Informatics and Telematics, Harokopio University of Athens, Kallithea, Greece

2. Department of Electrical Engineering, College of Engineering, Qatar University, Doha, Qatar

3. KINDI Computing Research Centre, College of Engineering, Qatar University, Doha, Qatar

Funder

National Priorities Research Programme

Qatar National Research Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8782664/10375894/10517657.pdf?arnumber=10517657

Reference127 articles.

1. A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

2. Two Decades of Recommender Systems at Amazon.com

3. Measuring the Business Value of Recommender Systems

4. Latest trends of security and privacy in recommender systems: A comprehensive review and future perspectives

5. Blockchain-based recommender systems: Applications, challenges and future opportunities

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