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Privacy-preserving Point-of-interest Recommendation based on Simplified Graph Convolutional Network for Geological Traveling

Published: 27 July 2024 Publication History

Abstract

The provision of privacy-preserving recommendations for geological tourist attractions is an important research area. The historical check-in data collected from location-based social networks (LBSNs) can be utilized to mine their preferences, thereby facilitating the promotion of the geological tourism industry. However, such check-ins often contain sensitive user information that poses privacy leakage risks. To address this issue, some methods have been proposed to develop privacy-preserving point-of-interest (POI) recommendation systems. These methods commonly rely on either perturbation-based or federated learning techniques to protect users’ privacy. However, the former can hinder preference capture, while the latter remains vulnerable to privacy breaches during the parameter-sharing process. To overcome these challenges, we propose a novel privacy-preserving POI recommendation model that incorporates users’ privacy preferences based on a simplified graph convolutional neural network. Specifically, we employ a generative model to create a subset of POIs that reflect users’ preferences but do not reveal their private information, and then we design a simplified graph convolutional network to analyze the high-order connectivity between users and POIs that are privacy-preserving. The resulting model enables efficient POI recommendation under strict privacy protection, which is particularly relevant to geological tourism. Experimental results on two public datasets demonstrate the effectiveness of our proposed approach.

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Information & Contributors

Information

Published In

cover image ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology  Volume 15, Issue 4
August 2024
563 pages
EISSN:2157-6912
DOI:10.1145/3613644
  • Editor:
  • Huan Liu
Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 July 2024
Online AM: 04 September 2023
Accepted: 17 August 2023
Revised: 29 June 2023
Received: 09 May 2023
Published in TIST Volume 15, Issue 4

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Author Tags

  1. Generative model
  2. point-of-interest recommendation
  3. graph convolutional network

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  • Research-article

Funding Sources

  • Foundation of Yunnan Key Laboratory of Service Computing
  • Natural Science Foundation of Shandong Province
  • ARC DECRA

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  • (2025)A survey on point-of-interest recommendations leveraging heterogeneous dataInformation Technology & Tourism10.1007/s40558-024-00301-3Online publication date: 4-Jan-2025
  • (2025)Efficient GNN-based social recommender systems through social graph refinementThe Journal of Supercomputing10.1007/s11227-024-06682-w81:1Online publication date: 1-Jan-2025
  • (2024)SABTR: semantic analysis-based tourism recommendationFrontiers in Physics10.3389/fphy.2024.149136512Online publication date: 17-Oct-2024
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