@article{Perifanis_FedPOIRec_2023,
author = {Perifanis, Vasileios and Drosatos, George and Stamatelatos, Giorgos and Efraimidis, Pavlos S.},
title = {FedPOIRec: Privacy-Preserving Federated POI Recommendation with Social Influence},
journal = {Information Sciences},
volume = {623},
year = {2023},
url = {https://www.sciencedirect.com/science/article/pii/S0020025522015171},
issn = {0020-0255},
pages = {767-790},
keywords = {Federated learning, Privacy, POI recommendation, Fully homomorphic encryption, Social network},
abstract = {With the growing number of Location-Based Social Networks, privacy-preserving point-of-interest (POI) recommendation has become a critical challenge when helping users discover potentially interesting new places. Traditional systems take a centralized approach that requires the transmission and collection of private user data. In this work, we present FedPOIRec, a privacy-preserving federated learning approach enhanced with features from user social circles to generate top-N POI recommendations. First, the FedPOIRec framework is built on the principle that local data never leave the owner’s device, while a parameter server blindly aggregates the local updates. Second, the local recommender results are personalized by allowing users to exchange their learned parameters, enabling knowledge transfer among friends. To this end, we propose a privacy-preserving protocol for integrating the preferences of the user’s friends, after the federated computation, by exploiting the properties of the Cheon-Kim-Kim-Song (CKKS) fully homomorphic encryption scheme. To evaluate FedPOIRec, we apply our approach to five real-world datasets using two recommendation models. Extensive experiments demonstrate that FedPOIRec achieves comparable recommendation quality to centralized approaches, while the social integration protocol incurs low computation and communication overhead on the user device.}
doi = {10.1016/j.ins.2022.12.024},
publisher = {Elsevier}
}