下一个兴趣点推荐算法综述

doi:10.3778/j.issn.1673-9418.2408046

摘要/Abstract

摘要： 随着移动互联网和位置服务的迅速发展，基于位置的社交网络已成为用户日常生活的一部分。深度学习技术，尤其是基于注意力机制和图神经网络的模型，在预测用户未来可能访问地点的下一个兴趣点推荐任务中取得了显著突破。系统回顾了过去五年内下一个兴趣点推荐算法的研究进展，重点分析了注意力机制和图神经网络在该领域的应用。介绍了相关技术的基础理论，包括注意力机制和图神经网络的基本原理及其在时空数据中的优势。通过对不同方法的优缺点进行深入比较和分析，总结了当前该领域所面临的主要挑战。详细介绍并比较了常用的下一个兴趣点推荐数据集（如Foursquare、Gowalla），并探讨了在实际应用中如何选择和利用这些数据集。讨论了推荐算法中常用的评价指标（如准确率、召回率）。展望了未来的研究方向，并提出了多种可能优化下一个兴趣点推荐算法性能的策略，包括解决深度模型中的数据稀疏性问题，提升模型可解释性，以及应对冷启动问题等。

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

刘广腾, 王峰, 吴中博. 下一个兴趣点推荐算法综述[J]. 计算机科学与探索, 2025, 19(7): 1747-1770.

LIU Guangteng, WANG Feng, WU Zhongbo. Survey on Next Point-of-Interest Recommendation Algorithms[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(7): 1747-1770.

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