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.
@article{arxiv.2208.01375,
title = {BERT4Loc: BERT for Location -- POI Recommender System},
author = {Syed Raza Bashir and Shaina Raza and Vojislav Misic},
journal= {arXiv preprint arXiv:2208.01375},
year = {2023}
}