  Location-based Social Networks (LBSNs) enable users to socialize with friends
and acquaintances by sharing their check-ins, opinions, photos, and reviews.
Huge volume of data generated from LBSNs opens up a new avenue of research that
gives birth to a new sub-field of recommendation systems, known as
Point-of-Interest (POI) recommendation. A POI recommendation technique
essentially exploits users' historical check-ins and other multi-modal
information such as POI attributes and friendship network, to recommend the
next set of POIs suitable for a user. A plethora of earlier works focused on
traditional machine learning techniques by using hand-crafted features from the
dataset. With the recent surge of deep learning research, we have witnessed a
large variety of POI recommendation works utilizing different deep learning
paradigms. These techniques largely vary in problem formulations, proposed
techniques, used datasets, and features, etc. To the best of our knowledge,
this work is the first comprehensive survey of all major deep learning-based
POI recommendation works. Our work categorizes and critically analyzes the
recent POI recommendation works based on different deep learning paradigms and
other relevant features. This review can be considered a cookbook for
researchers or practitioners working in the area of POI recommendation.
