Exploring market competition over topics in spatio-temporal document collections

• Main Authors: Zhao, Kaiqi, Cong, Gao, Chin, Jin-Yao, Wen, Rong
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Computer science and engineering; Topic Model; Exploratory Search
• Online Access: https://hdl.handle.net/10356/150170
• Institution: Nanyang Technological University

With the prominence of location-based services and social networks in recent years, huge amounts of spatio-temporal document collections (e.g., geo-tagged tweets) have been generated. These data collections often imply user’s ideas on different products and thus are helpful for business owners to explore hot topics of their brands and the competition relation to other brands in different spatial regions during different periods. In this work, we aim to mine the topics and the market competition of different brands over each topic for a category of business (e.g., coffeehouses) from spatio-temporal documents within a user-specified region and time period. To support such spatio-temporal search online in an exploratory manner, we propose a novel framework equipped by (1) a generative model for mining topics and market competition, (2) an Octree-based off-line pre-training method for the model and (3) an efficient algorithm for combining pre-trained models to return the topics and market competition on each topic within a user-specified pair of region and time span. Extensive experiments show that our framework is able to improve the runtime by up to an order of magnitude compared with baselines while achieving similar model quality in terms of training log-likelihood.