关键词: 下一个兴趣点推荐, 注意力机制, 图神经网络, 数据稀疏性, 冷启动问题

Abstract: With the rapid development of mobile Internet and location-based services, location-based social networks have become an integral part of users?? daily lives. Deep learning techniques, particularly models based on attention mechanisms and graph neural networks, have achieved significant breakthroughs in the task of recommending the next point-of-interest that users are likely to visit in the future. This paper provides a systematic review of the research progress in next point-of-interest recommendation algorithms over the past five years, focusing on the application of attention mechanisms and graph neural networks in this field. Firstly, the paper introduces the basic theories of related technologies, including the fundamental principles of attention mechanisms and graph neural networks, as well as their advantages in spatiotemporal data. By conducting an in-depth comparison and analysis of the strengths and weaknesses of different methods, the paper summarizes the main challenges currently faced in this field. Then, the paper provides a detailed introduction and comparison of commonly used next point-of-interest recommendation datasets (e.g., Foursquare, Gowalla), and discusses how to select and utilize these datasets in practical applications. In addition, the paper discusses common evaluation metrics used in recommendation algorithms (e.g., accuracy, recall rate). Finally, the paper explores potential future research directions and presents various possible strategies to optimize the performance of next point-of-interest recommendation algorithms, including addressing the data sparsity problem in deep models, enhancing model explainability, and tackling the cold start problem.

Key words: next point-of-interest recommendation, attention mechanism, graph neural network, data sparsity, cold start problem