  The next Point of Interest (POI) recommendation task is to predict users'
immediate next POI visit given their historical data. Location-Based Social
Network (LBSN) data, which is often used for the next POI recommendation task,
comes with challenges. One frequently disregarded challenge is how to
effectively use the abundant contextual information present in LBSN data.
Previous methods are limited by their numerical nature and fail to address this
challenge. In this paper, we propose a framework that uses pretrained Large
Language Models (LLMs) to tackle this challenge. Our framework allows us to
preserve heterogeneous LBSN data in its original format, hence avoiding the
loss of contextual information. Furthermore, our framework is capable of
comprehending the inherent meaning of contextual information due to the
inclusion of commonsense knowledge. In experiments, we test our framework on
three real-world LBSN datasets. Our results show that the proposed framework
outperforms the state-of-the-art models in all three datasets. Our analysis
demonstrates the effectiveness of the proposed framework in using contextual
information as well as alleviating the commonly encountered cold-start and
short trajectory problems.
