Next Point-of-Interest (POI) recommendation is a critical task in location-based services that aim to provide personalized suggestions for the user's next destination. Previous works on POI recommendation have laid focused on modeling the user's spatial preference. However, existing works that leverage spatial information are only based on the aggregation of users' previous visited positions, which discourages the model from recommending POIs in novel areas. This trait of position-based methods will harm the model's performance in many situations. Additionally, incorporating sequential information into the user's spatial preference remains a challenge. In this paper, we propose Diff-POI: a Diffusion-based model that samples the user's spatial preference for the next POI recommendation. Inspired by the wide application of diffusion algorithm in sampling from distributions, Diff-POI encodes the user's visiting sequence and spatial character with two tailor-designed graph encoding modules, followed by a diffusion-based sampling strategy to explore the user's spatial visiting trends. We leverage the diffusion process and its reversed form to sample from the posterior distribution and optimized the corresponding score function. We design a joint training and inference framework to optimize and evaluate the proposed Diff-POI. Extensive experiments on four real-world POI recommendation datasets demonstrate the superiority of our Diff-POI over state-of-the-art baseline methods. Further ablation and parameter studies on Diff-POI reveal the functionality and effectiveness of the proposed diffusion-based sampling strategy for addressing the limitations of existing methods.

翻译：下一个兴趣点推荐是位置服务中的关键任务，旨在为用户的下一个目的地提供个性化建议。已有的兴趣点推荐工作主要集中于建模用户的空间偏好。然而，利用空间信息的现有方法仅基于用户历史访问位置的聚合，这阻碍了模型推荐新区域中的兴趣点。这种基于位置的方法特性在许多情况下会损害模型性能。此外，将序列信息融入用户空间偏好仍是一个挑战。本文提出Diff-POI：一种基于扩散模型的采样用户空间偏好用于下一个兴趣点推荐的方法。受扩散算法在分布采样中的广泛应用启发，Diff-POI通过两个定制图编码模块对用户的访问序列和空间特征进行编码，并结合基于扩散的采样策略来探索用户的空间访问趋势。我们利用扩散过程及其逆过程从后验分布中采样，并优化相应的得分函数。我们设计了一个联合训练与推理框架来优化和评估所提出的Diff-POI。在四个真实兴趣点推荐数据集上的大量实验表明，Diff-POI优于现有的最优基线方法。针对Diff-POI的消融实验和参数研究揭示了所提出的基于扩散的采样策略在解决现有方法局限性方面的功能性与有效性。