摘要：

在社交网络中, 用户移动行为由时间周期性、地理邻近性及语义类别偏好共同驱动, 且交互数据高度稀疏。现有方法多侧重于对用户序列进行建模, 往往难以统一捕捉并保障上述时空语义因素之间的复杂一致性关联, 导致从稀疏数据中学习的模式鲁棒性不足。因此, 本文提出整体时空一致性的概念, 综合考虑用户-兴趣点(POI)联合预测任务中各个阶段的时间一致性和空间一致性, 实现地理维度和类别维度的协同预测。具体而言, 本文基于时间、地理坐标和语义类别这三维特征空间, 兼顾地理-时间和类别-时间之间的时间一致性以及地理-类别之间的空间一致性, 在特征空间嵌入、影响因素表示、影响因素解耦合、影响因素融合推断等阶段引入相应的一致性约束, 从而构建改进的解纠缠图嵌入预测模型。模型首先对地理空间和类别空间的特征嵌入引入基于聚合依赖的空间一致性约束; 然后利用图神经网络提取了5类影响因素, 并通过时-空双域并行的影响因素解耦合方式, 实现了基于时间一致性的解纠缠学习; 最后在地理维度和类别维度依次开展影响因素融合推断, 基于地理坐标预测结果及类别聚合依赖关系得到语义类别预测。实验结果表明, 本文方法在Foursquare数据集上基本优于基线模型, 其中嵌入层聚合模块的移除使预测任务的受试者工作特征(ROC)曲线下的面积(AUC)和log loss分别相对最佳基线退化了6.13%和36.29%, 是一种非常高效的时空语义多重一致性建模手段, 推断层聚合模块的增益与数据规模相关, 可以对预测结果提供细粒度调整, 而时序特征模块在签到数据稀疏的条件下能够为模型提供重要的行为先验信息。

关键词: 兴趣点预测, 整体时空一致性, 自监督解纠缠, 图嵌入, 联合预测

Abstract:

In social networks, users' mobile behavior is jointly driven by temporal periodicity, geographical proximity, and semantic category preferences. However, interaction data are often highly sparse. Existing methods mostly focus on modeling user sequences, often failing to uniformly capture and ensure the complex consistency associations among the above spatiotemporal semantic factors, resulting in insufficient robustness of the learned patterns from sparse data. Therefore, this paper proposes the concept of overall spatiotemporal consistency, which comprehensively considering the temporal and spatial consistency at each stage of the user and Point-of-Interest (POI) joint prediction task, to achieve collaborative geography-wise and category-wise prediction. Specifically, this study considers the three-dimensional feature space of time, geographical coordinates, and semantic categories, as well as the temporal consistency between geography-time and category-time space and the spatial consistency between geography-category space. Corresponding consistency constraints are introduced in the feature space embedding, influence representation, influence decoupling, and influence-based fusion inference stages to construct an improved disentangled graph embedding prediction model. The model first introduces a spatial consistency constraint based on the aggregation dependency between geography-category embeddings. Then, it uses a graph neural network to extract five types of influence factors and achieves disentangled learning based on temporal consistency through a time-space dual-domain parallel influence decoupling method. Finally, it obtains semantic category prediction based on the geographical coordinate prediction results and the category aggregation dependencies, interacting with spatial consistency between geographical and categorical dimensions. Experimental results demonstrate that the proposed method is superior to baseline models on the Foursquare dataset. Removing the embedding layer aggregation module reduces the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) and log loss of the prediction task by 6.13% and 36.29%, respectively, compared with the best baseline. This is a highly efficient spatiotemporal semantic multi-consistency modeling approach. The gain of the inference layer aggregation module is related to the data scale and can provide fine-grained adjustments to the prediction results. The temporal feature module can provide important behavioral prior information for the model under the condition of sparse check-in data.

Key words: Point-of-Interest (POI) prediction, overall spatio-temporal consistency, self-supervised disentanglement, graph embedding, joint prediction