Discovering Spatio-Temporal Latent Influence in Geographical Attention Dynamics
Discovering Spatio-Temporal Latent Influence in Geographical Attention Dynamics
复制标题
DOI：
10.1007/978-3-030-10928-8_31
复制
发表时间：
2018-09
期刊：
影响因子：
--
通讯作者：
Minoru Higuchi;Kanji Matsutani;Masahito Kumano;M. Kimura
中科院分区：
文献类型：
--
作者：
Minoru Higuchi;Kanji Matsutani;Masahito Kumano;M. Kimura
我们解决了在连续时间轴和连续空间域的背景下，对观光城市中有吸引力的景点（称为兴趣点（POIs））的事件发生过程进行建模的问题，这被称为地理注意力动力学建模。本文将Hawkes过程与时变高斯混合模型新颖地结合起来，并引入时隙影响结构，从地理注意动力学的角度出发，提出了主要观光区时空影响结构的概率模型，旨在准确预测近期景点访问事件。我们开发了一种有效的方法，从观测到的POI访问事件序列中推断出该模型中的参数，并提出了一种地理注意力动态分析方法。利用日本某观光城市POI访问事件的真实数据，验证了该模型在预测精度上优于传统模型，并从地理注意力动态的角度揭示了城市主要观光区域间的时空影响结构。
We address the problem of modeling the occurrence process of events for visiting attractive places, called points-of-interest (POIs), in a sightseeing city in the setting of a continuous time-axis and a continuous spatial domain, which is referred to as modeling geographical attention dynamics. By combining a Hawkes process with a time-varying Gaussian mixture model in a novel way and incorporating the influence structure depending on time slots as well, we propose a probabilistic model for discovering the spatio-temporal influence structure among major sightseeing areas from the viewpoint of geographical attention dynamics, and aim to accurately predict POI visit events in the near future. We develop an efficient method of inferring the parameters in the proposed model from the observed sequence of POI visit events, and present an analysis method for the geographical attention dynamics. Using real data of POI visit events in a Japanese sightseeing city, we demonstrate that the proposed model outperforms conventional models in terms of predictive accuracy, and uncover the spatio-temporal influence structure among major sightseeing areas in the city from the perspective of geographical attention dynamics.