A survey on graph neural network-based next POI recommendation for smart cities

被引：1
作者：
Yu, Jian [1 ]
Guo, Lucas [1 ]
Zhang, Jiayu [2 ]
Wang, Guiling [2 ]
机构：
[1] Department of Computer Science and Soft Engineering, Auckland University of Technology, Auckland
[2] School of Information Science and Technology, Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data, North China University of Technology, Beijing
来源：
Journal of Reliable Intelligent Environments | 2024年 / 10卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Graph neural networks (GNNs); Location-based services; Next point-of-interest (POI) recommendation; Smart city;
D O I：
10.1007/s40860-024-00233-z
中图分类号：
学科分类号：
摘要：
Amid the rise of mobile technologies and Location-Based Social Networks (LBSNs), there’s an escalating demand for personalized Point-of-Interest (POI) recommendations. Especially pivotal in smart cities, these systems aim to enhance user experiences by offering location recommendations tailored to past check-ins and visited POIs. Distinguishing itself from traditional POI recommendations, the next POI approach emphasizes predicting the immediate subsequent location, factoring in both geographical attributes and temporal patterns. This approach, while promising, faces with challenges like capturing evolving user preferences and navigating data biases. The introduction of Graph Neural Networks (GNNs) brings forth a transformative solution, particularly in their ability to capture high-order dependencies between POIs, understanding deeper relationships and patterns beyond immediate connections. This survey presents a comprehensive exploration of GNN-based next POI recommendation approaches, delving into their unique characteristics, inherent challenges, and potential avenues for future research. © The Author(s) 2024.
引用
收藏
页码：299 / 318
页数：19
相关论文
共 69 条
  • [1] Liu Q., Wu S., Wang L., Tan T., Predicting the next location: a recurrent model with spatial and temporal contexts, Proceedings of the AAAI Conference on Artificial Intelligence, 30, 1, (2016)
  • [2] Sun K., Qian T., Chen T., Liang Y., Nguyen Q.V.H., Yin H., Where to go next: Modeling long- and short-term user preferences for point-of-interest recommendation, Proceedings of the AAAI Conference on Artificial Intelligence, 34, 1, pp. 214-221, (2020)
  • [3] Kong D., Wu F., Hst-lstm: A hierarchical spatial-temporal long-short term memory network for location prediction, Proceedings of the 27Th International Joint Conference on Artificial Intelligence, pp. 2341-2347, (2018)
  • [4] Salakhutdinov R., Mnih A., Probabilistic matrix factorization, Proceedings of the 20Th International Conference on Neural Information Processing Systems. NIPS’07, pp. 1257-1264, (2007)
  • [5] He J., Li X., Liao L., Song D., Cheung W., Inferring a personalized next point-of-interest recommendation model with latent behavior patterns, Proceedings of the AAAI Conference on Artificial Intelligence, 30, 1, (2016)
  • [6] Islam M.A., Mohammad M.M., Sarathi Das S.S., Ali M.E., A survey on deep learning based point-of-interest (POI) recommendations, Neurocomputing, 472, pp. 306-325, (2022)
  • [7] Werneck H., Silva N., Viana M.C., Mourao F., Pereira A.C.M., Rocha L., A survey on point-of-interest recommendation in location-based social networks, Proceedings of the Brazilian Symposium on Multimedia and the Web. Webmedia ’20, pp. 185-192, (2020)
  • [8] Silva B.N., Khan M., Han K., Towards sustainable smart cities: a review of trends, architectures, components, and open challenges in smart cities, Sustain Cities Soc, 38, pp. 697-713, (2018)
  • [9] Hornos M.J., Quinde M., Development methodologies for iot-based systems: Challenges and research directions, To Appear in Anniversary Issue of the Journal of Reliable Intelligent Environments, 10, 3, (2024)
  • [10] Lu Z., Afridi I., Kang H.J., Ruchkin I., Zheng X., Surveying neuro-symbolic approaches for reliable artificial intelligence of things, To Appear in Anniversary Issue of the Journal of Reliable Intelligent Environments, 10, 3, (2024)
1234567