  Human mobility data accumulated from Point-of-Interest (POI) check-ins
provides great opportunity for user behavior understanding. However, data
quality issues (e.g., geolocation information missing, unreal check-ins, data
sparsity) in real-life mobility data limit the effectiveness of existing
POI-oriented studies, e.g., POI recommendation and location prediction, when
applied to real applications. To this end, in this paper, we develop a model,
named Bi-STDDP, which can integrate bi-directional spatio-temporal dependence
and users' dynamic preferences, to identify the missing POI check-in where a
user has visited at a specific time. Specifically, we first utilize
bi-directional global spatial and local temporal information of POIs to capture
the complex dependence relationships. Then, target temporal pattern in
combination with user and POI information are fed into a multi-layer network to
capture users' dynamic preferences. Moreover, the dynamic preferences are
transformed into the same space as the dependence relationships to form the
final model. Finally, the proposed model is evaluated on three large-scale
real-world datasets and the results demonstrate significant improvements of our
model compared with state-of-the-art methods. Also, it is worth noting that the
proposed model can be naturally extended to address POI recommendation and
location prediction tasks with competitive performances.
