标题： 解耦多维时空转移图表示学习以增强社交POI推荐 显示英文标题

标题： Disentangling Multiplex Spatial-Temporal Transition Graph Representation Learning for Socially Enhanced POI Recommendation

摘要： 下一兴趣点（POI）推荐是商业智能中的研究热点，其中用户的空间时间转移和社会关系起着关键作用。 然而，大多数现有工作分别建模空间和时间转移，导致相同的空间时间关键节点表示不一致。 这种不一致在融合过程中引入了冗余信息，增加了模型不确定性并降低了可解释性。 为了解决这个问题，我们提出了DiMuST，一种基于多层时空转移图的解耦表示学习的社会增强POI推荐模型。 该模型采用了一种新颖的解耦变分多层图自动编码器（DAE），首先使用多层时空图策略解耦共享和私有分布。 然后通过专家产品（PoE）机制融合共享特征，并通过对比约束对私有特征进行去噪。 该模型有效捕捉了POIs的空间时间转移表示，同时保留了其空间时间关系的内在相关性。 在两个具有挑战性的数据集上的实验表明，我们的DiMuST在多个指标上显著优于现有方法。 显示英文摘要

摘要： Next Point-of-Interest (POI) recommendation is a research hotspot in business intelligence, where users' spatial-temporal transitions and social relationships play key roles. However, most existing works model spatial and temporal transitions separately, leading to misaligned representations of the same spatial-temporal key nodes. This misalignment introduces redundant information during fusion, increasing model uncertainty and reducing interpretability. To address this issue, we propose DiMuST, a socially enhanced POI recommendation model based on disentangled representation learning over multiplex spatial-temporal transition graphs. The model employs a novel Disentangled variational multiplex graph Auto-Encoder (DAE), which first disentangles shared and private distributions using a multiplex spatial-temporal graph strategy. It then fuses the shared features via a Product of Experts (PoE) mechanism and denoises the private features through contrastive constraints. The model effectively captures the spatial-temporal transition representations of POIs while preserving the intrinsic correlation of their spatial-temporal relationships. Experiments on two challenging datasets demonstrate that our DiMuST significantly outperforms existing methods across multiple metrics.