Embracing LLMs for Point-of-Interest Recommendations

Abstract

A point-of-interest (POI) recommendation becomes the core function of location-based services. Unlike a traditional item recommendation, a POI recommendation has distinct features, such as geographical influences, complex mobility patterns, and a balance between local and global user preferences. Past POI recommendation system research has focused mainly on integrating deep learning models like convolutional neural networks, recurrent neural networks, and attention-based architectures, demonstrating their effectiveness in addressing the dynamic nature of spatial–temporal data in POI recommendation areas. In recent years, with the rise of large language models (LLMs), POI recommendation has produced a number of promising directions. This article first discusses the characteristics and state-of-the-art solutions of POI recommendation, then it introduces potential research directions by integrating the latest LLMs.