  Recommending points of interest (POIs) is a challenging task that requires
extracting comprehensive location data from location-based social media
platforms. To provide effective location-based recommendations, it's important
to analyze users' historical behavior and preferences. In this study, we
present a sophisticated location-aware recommendation system that uses
Bidirectional Encoder Representations from Transformers (BERT) to offer
personalized location-based suggestions. Our model combines location
information and user preferences to provide more relevant recommendations
compared to models that predict the next POI in a sequence. Our experiments on
two benchmark dataset show that our BERT-based model outperforms various
state-of-the-art sequential models. Moreover, we see the effectiveness of the
proposed model for quality through additional experiments.
