  There exists a high variability in mobility data volumes across different
regions, which deteriorates the performance of spatial recommender systems that
rely on region-specific data. In this paper, we propose a novel transfer
learning framework called REFORMD, for continuous-time location prediction for
regions with sparse checkin data. Specifically, we model user-specific
checkin-sequences in a region using a marked temporal point process (MTPP) with
normalizing flows to learn the inter-checkin time and geo-distributions. Later,
we transfer the model parameters of spatial and temporal flows trained on a
data-rich origin region for the next check-in and time prediction in a target
region with scarce checkin data. We capture the evolving region-specific
checkin dynamics for MTPP and spatial-temporal flows by maximizing the joint
likelihood of next checkin with three channels (1) checkin-category prediction,
(2) checkin-time prediction, and (3) travel distance prediction. Extensive
experiments on different user mobility datasets across the U.S. and Japan show
that our model significantly outperforms state-of-the-art methods for modeling
continuous-time sequences. Moreover, we also show that REFORMD can be easily
adapted for product recommendations i.e., sequences without any spatial
component.
