  Recent years have witnessed the increasing popularity of Location-based
Social Network (LBSN) services, which provides unparalleled opportunities to
build personalized Point-of-Interest (POI) recommender systems. Existing POI
recommendation and location prediction tasks utilize past information for
future recommendation or prediction from a single direction perspective, while
the missing POI category identification task needs to utilize the check-in
information both before and after the missing category. Therefore, a
long-standing challenge is how to effectively identify the missing POI
categories at any time in the real-world check-in data of mobile users. To this
end, in this paper, we propose a novel neural network approach to identify the
missing POI categories by integrating both bi-directional global non-personal
transition patterns and personal preferences of users. Specifically, we
delicately design an attention matching cell to model how well the check-in
category information matches their non-personal transition patterns and
personal preferences. Finally, we evaluate our model on two real-world
datasets, which clearly validate its effectiveness compared with the
state-of-the-art baselines. Furthermore, our model can be naturally extended to
address next POI category recommendation and prediction tasks with competitive
performance.
