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DOI: 10.14569/IJACSA.2023.0140460
PDF

Fusion Privacy Protection of Graph Neural Network Points of Interest Recommendation

Author 1: Yong Gan
Author 2: ZhenYu Hu

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 4, 2023.

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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.

Keywords: Recommendation algorithms; location protection; graph convolutional neural networks; k-anonymity

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}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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