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：下一点兴趣点推荐是基于位置的服务中的关键任务，旨在为用户的下一个目的地提供个性化建议。以往关于POI推荐的研究主要聚焦于用户空间偏好的建模。然而，现有利用空间信息的方法仅基于用户历史访问位置的聚合，这限制了模型推荐新区域POI的能力。这种基于位置的方法的特性在许多场景下会损害模型性能。此外，将序列信息融入用户空间偏好仍具有挑战性。本文提出Diff-POI：一种基于扩散模型的框架，通过采样用户空间偏好实现下一个POI推荐。受扩散算法在分布采样中广泛应用的启发，Diff-POI采用两个定制设计的图编码模块对用户访问序列和空间特征进行编码，并基于扩散采样策略探索用户空间访问趋势。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。设计了联合训练与推理框架以优化和评估所提出的Diff-POI。在四个真实POI推荐数据集上的大量实验表明，Diff-POI在性能上优于最先进的基线方法。针对Diff-POI的消融实验与参数研究揭示了所提出的扩散采样策略在解决现有方法局限性方面的功能与有效性。