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：下一兴趣点推荐是位置服务中的关键任务，旨在为用户提供个性化目的地建议。现有相关工作主要聚焦于用户空间偏好建模，但其利用空间信息的方式仅基于历史访问位置的聚合，这阻碍模型对新兴区域兴趣点的推荐能力。这种基于位置的方法特性会在多种场景下损害模型性能，且如何将序列信息融入用户空间偏好建模仍是挑战。本文提出Diff-POI：一种基于扩散模型的下一个兴趣点推荐方法，通过采样用户空间偏好实现推荐。受扩散算法在分布采样中广泛应用启发，Diff-POI采用两个定制化图编码模块分别编码用户访问序列与空间特征，进而通过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过程从后验分布中采样，并优化相应得分函数。设计联合训练与推理框架对Diff-POI进行优化与评估。在四个真实兴趣点推荐数据集上的大量实验表明，Diff-POI优于当前最优基线方法。针对Diff-POI的消融实验与参数研究揭示了所提出基于扩散的采样策略在突破现有方法局限中的功能性与有效性。