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

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

In the age of information overload, recommender systems have emerged as essential tools, assisting users in decision-making processes by offering personalized suggestions. However, their effectiveness is contingent on the availability of large amounts of user data, raising significant privacy and se...

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Bibliographic Details
Main Authors: Christos Chronis, Iraklis Varlamis, Yassine Himeur, Aya N. Sayed, Tamim M. AL-Hasan, Armstrong Nhlabatsi, Faycal Bensaali, George Dimitrakopoulos
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Open Journal of the Computer Society
Subjects:
Recommender systems
federated learning (FL)
privacy-preserving techniques
distributed learning
Online Access:https://ieeexplore.ieee.org/document/10517657/

Internet

https://ieeexplore.ieee.org/document/10517657/

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