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：下一兴趣点（POI）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。现有POI推荐研究主要关注用户空间偏好的建模。然而，当前利用空间信息的方法仅基于用户历史访问位置的聚合，这阻碍了模型在新颖区域推荐POI。这种基于位置的方法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挑战性。本文提出Diff-POI：一种基于扩散模型的方案，用于采样用户对下一POI推荐的空间偏好。受扩散算法在分布采样中广泛应用的启发，Diff-POI通过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，并采用基于扩散的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形式从后验分布中采样，并优化相应的得分函数。设计联合训练与推理框架对所提Diff-POI进行优化与评估。在四个真实POI推荐数据集上的广泛实验表明，本模型优于现有最先进基线方法。针对Diff-POI的消融实验与参数研究进一步揭示了所提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。