  The rapid growth of location-based services (LBS) has yielded massive amounts
of data on human mobility. Effectively extracting meaningful representations
for user-generated check-in sequences is pivotal for facilitating various
downstream services. However, the user-generated check-in data are
simultaneously influenced by the surrounding objective circumstances and the
user's subjective intention. Specifically, the temporal uncertainty and spatial
diversity exhibited in check-in data make it difficult to capture the
macroscopic spatial-temporal patterns of users and to understand the semantics
of user mobility activities. Furthermore, the distinct characteristics of the
temporal and spatial information in check-in sequences call for an effective
fusion method to incorporate these two types of information. In this paper, we
propose a novel Spatial-Temporal Cross-view Contrastive Representation (STCCR)
framework for check-in sequence representation learning. Specifically, STCCR
addresses the above challenges by employing self-supervision from "spatial
topic" and "temporal intention" views, facilitating effective fusion of spatial
and temporal information at the semantic level. Besides, STCCR leverages
contrastive clustering to uncover users' shared spatial topics from diverse
mobility activities, while employing angular momentum contrast to mitigate the
impact of temporal uncertainty and noise. We extensively evaluate STCCR on
three real-world datasets and demonstrate its superior performance across three
downstream tasks.
