USTAR: Online Multimodal Embedding for Modeling User-Guided Spatiotemporal Activity

--Building spatiotemporal activity models for people's activities in urban spaces is important for understanding the ever-increasing complexity of urban dynamics. With the emergence of Geo-T agged Social Media (GTSM) records, previous studies demonstrate the potential of GTSM records for spatiotemporal activity modeling. However, they ignore Non-GeoT agged Social Media (NGTSM) records, which generally account for the majority of posts (e.g., more than 95% in Twitter), and could represent a great source of information to alleviate the sparsity of GTSM records. Furthermore, in the current spatiotemporal embedding techniques, less focus has been given to the users, who exhibit spatially motivated behaviors. T o bridge this research gap, this work proposes USTAR, a novel online learning method for User-guided SpatioT emporal Activity Representation, which (1) embeds locations, time, and text along with users into the same embedding space to capture their correlations; (2) uses a novel collaborative filtering approach based on two different empirically studied user behaviors to incorporate both NGTSM and GTSM records in learning; and (3) introduces a novel sampling technique to learn spatiotemporal representations in an online fashion to accommodate recent information into the embedding space, while avoiding overfitting to recent records and frequently appearing units in social media streams. Our results show that USTAR substantially improves the state-of-the-art for region retrieval and keyword retrieval and its potential to be applied to other downstream applications such as local event detection. With urbanization, more than half of the today's world population (exactly 55.7% as of 2019 1) live in urban areas. It is projected that the urbanization trend will gradually increase over the next few decades. As a result, it is not only difficult to tackle urban challenges (e.g., controlling traffic congestion, controlling environmental pollution), it is difficult for people in urban areas to find the most suitable activities and places at the right time. For instance, consider an inhabitant in a highly urbanized city like Melbourne. What is the best time to visit Mount Buller, a snowy mountain near Melbourne, for skiing? Up until the early 2000s 2, it was almost impossible to model these complex urban dynamics due to the lack of reliable data sources.