Fusion Privacy Protection of Graph Neural Network Points of Interest Recommendation

Abstract: For the rapidly developing location-based web recommendation services, traditional point-of-interest(POI) recommendation methods not only fail to utilize user information efficiently, but also face the problem of privacy leakage. Therefore, this paper proposes a privacy-preserving interest point recommendation system that fuses location and user interaction information. The geolocation-based recommendation system uses convolutional neural networks (CNN) to extract the correlation between user and POI interactions and fuse text features, and then combine the location check-in probability to recommend POIs to users. To address the geolocation leakage problem, this paper proposes an algorithm that integrates k-anonymization techniques with homogenized coordinates (KMG) to generalize the real location of users. Finally, this paper integrates location-preserving algorithms and recommendation algorithms to build a privacy-preserving recommendation system. The system is analyzed by information entropy theory and has a high privacy-preserving effect. The experimental results show that the proposed recommendation system has better recommendation performance on the basis of privacy protection compared with other recommendation algorithms.

Yong Gan and ZhenYu Hu. “Fusion Privacy Protection of Graph Neural Network Points of Interest Recommendation”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.4 (2023). http://dx.doi.org/10.14569/IJACSA.2023.0140460

@article{Gan2023,
title = {Fusion Privacy Protection of Graph Neural Network Points of Interest Recommendation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140460},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140460},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {4},
author = {Yong Gan and ZhenYu Hu}
}