Fusion Privacy Protection of Graph Neural Network Points of Interest Recommendation

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.

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