  Location prediction forecasts a user's location based on historical user
mobility traces. To tackle the intrinsic sparsity issue of real-world user
mobility traces, spatiotemporal contexts have been shown as significantly
useful. Existing solutions mostly incorporate spatiotemporal distances between
locations in mobility traces, either by feeding them as additional inputs to
Recurrent Neural Networks (RNNs) or by using them to search for informative
past hidden states for prediction. However, such distance-based methods fail to
capture the time-varying temporal regularities of human mobility, where human
mobility is often more regular in the morning than in other periods, for
example; this suggests the usefulness of the actual timestamps besides the
temporal distances. Against this background, we propose REPLAY, a general RNN
architecture learning to capture the time-varying temporal regularities for
location prediction. Specifically, REPLAY not only resorts to the
spatiotemporal distances in sparse trajectories to search for the informative
past hidden states, but also accommodates the time-varying temporal
regularities by incorporating smoothed timestamp embeddings using Gaussian
weighted averaging with timestamp-specific learnable bandwidths, which can
flexibly adapt to the temporal regularities of different strengths across
different timestamps. Our extensive evaluation compares REPLAY against a
sizable collection of state-of-the-art techniques on two real-world datasets.
Results show that REPLAY consistently and significantly outperforms
state-of-the-art methods by 7.7\%-10.9\% in the location prediction task, and
the bandwidths reveal interesting patterns of the time-varying temporal
regularities.
