Discovering Spatio-Temporal Latent Influence in Geographical Attention Dynamics

• Published in: Machine Learning and Knowledge Discovery in Databases Vol. 11052; pp. 517 - 534
• Main Authors: Higuchi, Minoru, Matsutani, Kanji, Kumano, Masahito, Kimura, Masahiro
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Geographical attention dynamics; Point process model; Spatio-temporal influence structure
• ISBN: 3030109275; 9783030109271
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-10928-8_31

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.