Point of Interest

Point-of-Interest (POI) recommendation systems aim to predict users' next location based on their historical data and contextual information, enhancing user experience in location-based services. Current research heavily emphasizes the use of large language models (LLMs) and graph neural networks (GNNs) within various architectures, including multi-agent systems and federated learning, to improve recommendation accuracy, address data sparsity, and enhance privacy. These advancements are crucial for optimizing location-based services, improving urban mobility applications, and informing real-world decision-making processes across diverse sectors like tourism and real estate. Furthermore, significant effort is dedicated to mitigating biases and ensuring fairness in recommendation outcomes.