  Recently, Point of interest (POI) recommendation has gained ever-increasing
importance in various Location-Based Social Networks (LBSNs). With the recent
advances of neural models, much work has sought to leverage neural networks to
learn neural embeddings in a pre-training phase that achieve an improved
representation of POIs and consequently a better recommendation. However,
previous studies fail to capture crucial information about POIs such as
categorical information.
  In this paper, we propose a novel neural model that generates a POI embedding
incorporating sequential and categorical information from POIs. Our model
consists of a check-in module and a category module. The check-in module
captures the geographical influence of POIs derived from the sequence of users'
check-ins, while the category module captures the characteristics of POIs
derived from the category information. To validate the efficacy of the model,
we experimented with two large-scale LBSN datasets. Our experimental results
demonstrate that our approach significantly outperforms state-of-the-art POI
recommendation methods.
