  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-Tagged Social Media (GTSM) records,
previous studies demonstrate the potential of GTSM records for spatiotemporal
activity modeling. State-of-the-art methods for this task embed different
modalities (location, time, and text) of GTSM records into a single embedding
space. However, they ignore Non-GeoTagged 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. To bridge this research gap, this work proposes USTAR, a novel
online learning method for User-guided SpatioTemporal 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.
