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（POI）推荐是基于位置服务中的一项关键任务，旨在为用户的下一个目的地提供个性化建议。以往关于POI推荐的研究主要集中于建模用户的空间偏好。然而，现有利用空间信息的工作仅基于用户历史访问位置的聚合，这阻碍了模型推荐新地区POI的能力。这种基于位置的方法的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。在本文中，我们提出Diff-POI：一种基于扩散的模型，用于采样用户空间偏好以进行下一个POI推荐。受扩散算法在分布采样中广泛应用的启发，Diff-POI通过两个专门设计的图编码模块对用户的访问序列和空间特征进行编码，随后采用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，并优化相应的评分函数。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模型。在四个真实POI推荐数据集上的大量实验表明，我们的Diff-POI相较于现有最先进的基线方法具有优越性。进一步对Diff-POI进行的消融研究和参数分析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与有效性。