  Point-of-Interest recommendation is an increasing research and developing
area within the widely adopted technologies known as Recommender Systems. Among
them, those that exploit information coming from Location-Based Social Networks
(LBSNs) are very popular nowadays and could work with different information
sources, which pose several challenges and research questions to the community
as a whole. We present a systematic review focused on the research done in the
last 10 years about this topic. We discuss and categorize the algorithms and
evaluation methodologies used in these works and point out the opportunities
and challenges that remain open in the field. More specifically, we report the
leading recommendation techniques and information sources that have been
exploited more often (such as the geographical signal and deep learning
approaches) while we also alert about the lack of reproducibility in the field
that may hinder real performance improvements.
