The prevalence of location-based social networks (LBSNs) provides an opportunity for human mobility behavior understanding and prediction. However, data quality issues (e.g., historical check-in POI missing, data sparsity) always limit the effectiveness of existing LBSN-oriented studies, e.g., next Point-of-Interest (POI) recommendation or prediction. In contrast to previous efforts in the above study, we focus on identifying missing POIs that the user has visited at a past specific time and develop a category-aware multigraph embedding (CAME) model. Specifically, CAME jointly captures temporal cyclic effect, user preference, and sequential transition pattern in a unified way by embedding five relational information graphs into a shared dimensional space from both POI- and category-instance levels. The proposed model also incorporates region-level spatial proximity to explore the geographical influence and derives the ranking score list of candidates for missing POI identification. Extensive experiments against state-of-the-art methods are conducted on two real datasets, and the experimental results show its superiority over other competitors. Significantly, the proposed model can be naturally extended to next POI recommendation and prediction tasks with competitive performances.

基于位置的社交网络 (LBSN) 的流行为人类移动行为的理解和预测提供了机会。然而，数据质量问题（例如，历史签入 POI 缺失、数据稀疏）总是限制现有的面向 LBSN 的研究的有效性，例如，下一个兴趣点 (POI) 推荐或预测。与上述研究中先前的努力相比，我们专注于识别用户在过去特定时间访问过的缺失 POI，并开发一个类别感知的多图嵌入 (CAME) 模型。具体来说，CAME 通过将五个关系信息图嵌入到 POI 和类别实例级别的共享维度空间中，以统一的方式联合捕获时间循环效应、用户偏好和顺序转换模式。所提出的模型还结合了区域级的空间邻近度来探索地理影响，并得出缺失 POI 识别候选者的排名得分列表。在两个真实数据集上对最先进的方法进行了广泛的实验，实验结果表明其优于其他竞争对手。重要的是，所提出的模型可以自然地扩展到具有竞争性能的下一个 POI 推荐和预测任务。