HAP-SAP：基于潜在时空Hawkes过程的LBSN语义标注
HAP-SAP: Semantic Annotation in LBSNs using Latent Spatio-Temporal Hawkes Process
摘要：
基于位置的社交网络（LBSNs）的流行，使人们更容易理解人类的流动模式。人类动力学的知识可以以各种方式提供帮助，如城市规划，管理交通拥堵，个性化推荐等。这些动力学受到社会影响，移动周期性，空间邻近性，用户之间的影响和语义类别等因素的影响，这使得位置建模成为一项关键任务。然而，作为位置的语义表征的类别，可能会丢失一些签入，并可能对用户的移动动态建模产生不利影响。与此同时，移动模式提供了一个线索，对缺失的语义类别。在本文中，我们同时解决的问题，语义标注的位置和位置采用动态的用户。我们提出了我们的模型HAP-SAP，一个潜在的时空多变量霍克斯过程，它考虑了潜在的语义类别的影响，时间和空间的移动模式的用户。模型参数和潜在语义类别的推断使用期望最大化算法，该算法使用吉布斯抽样获得后验分布的潜在语义类别。推断的语义类别可以补充我们的模型预测用户的下一个签入事件。我们在真实数据集上的实验证明了所提出的模型的语义标注和位置采用建模任务的有效性。
英文摘要：
The prevalence of location-based social networks (LBSNs) has eased the understanding of human mobility patterns. Knowledge of human dynamics can aid in various ways like urban planning, managing traffic congestion, personalized recommendation etc. These dynamics are influenced by factors like social impact, periodicity in mobility, spatial proximity, influence among users and semantic categories etc., which makes location modelling a critical task. However, categories which act as semantic characterization of the location, might be missing for some check-ins and can adversely affect modelling the mobility dynamics of users. At the same time, mobility patterns provide a cue on the missing semantic category. In this paper, we simultaneously address the problem of semantic annotation of locations and location adoption dynamics of users. We propose our model HAP-SAP, a latent spatio-temporal multivariate Hawkes process, which considers latent semantic category influences, and temporal and spatial mobility patterns of users. The model parameters and latent semantic categories are inferred using expectation-maximization algorithm, which uses Gibbs sampling to obtain posterior distribution over latent semantic categories. The inferred semantic categories can supplement our model on predicting the next check-in events by users. Our experiments on real datasets demonstrate the effectiveness of the proposed model for the semantic annotation and location adoption modelling tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
