Large Language Models for Next Point-of-Interest Recommendation

The next point-of-interest (POI) recommendation task is to predict users' immediate next POI visit given their historical data. Locationbased social network 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 location-based social network 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 to tackle this challenge. Our framework allows us to preserve heterogeneous location-based social network 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 location-based social network 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. Our source code is available at: https://github.com/neolifer/LLM4POI