  A considerable amount of mobility data has been accumulated due to the
proliferation of location-based service. Nevertheless, compared with mobility
data from transportation systems like the GPS module in taxis, this kind of
data is commonly sparse in terms of individual trajectories in the sense that
users do not access mobile services and contribute their data all the time.
Consequently, the sparsity inevitably weakens the practical value of the data
even it has a high user penetration rate. To solve this problem, we propose a
novel attentional neural network-based model, named AttnMove, to densify
individual trajectories by recovering unobserved locations at a fine-grained
spatial-temporal resolution. To tackle the challenges posed by sparsity, we
design various intra- and inter- trajectory attention mechanisms to better
model the mobility regularity of users and fully exploit the periodical pattern
from long-term history. We evaluate our model on two real-world datasets, and
extensive results demonstrate the performance gain compared with the
state-of-the-art methods. This also shows that, by providing high-quality
mobility data, our model can benefit a variety of mobility-oriented down-stream
applications.
