GenUP: Generative User Profilers as In-Context Learners for Next POI Recommender Systems

TL;DR

This paper introduces GenUP, a method that creates natural language user profiles from social check-ins to improve POI recommendations, enhancing transparency, scalability, and accuracy using large language models.

Contribution

It proposes generating NL user profiles from social data and using them as prompts for LLMs, reducing data requirements and improving interpretability in POI recommendation systems.

Findings

Outperforms baseline methods in accuracy

Offers more scalable and resource-efficient recommendations

Provides enhanced transparency and interpretability

Abstract

Traditional Point-of-Interest (POI) recommendation systems often lack transparency, interpretability, and scrutability due to their reliance on dense vector-based user embeddings. Furthermore, the cold-start problem -- where systems have insufficient data for new users -- limits their ability to generate accurate recommendations. Existing methods often address this by leveraging similar trajectories from other users, but this approach can be computationally expensive and increases the context length for LLM-based methods, making them difficult to scale. To address these limitations, we propose a method that generates natural language (NL) user profiles from large-scale, location-based social network (LBSN) check-ins, utilizing robust personality assessments and behavioral theories. These NL profiles capture user preferences, routines, and behaviors, improving POI prediction accuracy while offering enhanced transparency. By incorporating NL profiles as system prompts to LLMs, our approach reduces reliance on extensive historical data, while remaining flexible, easily updated, and computationally efficient. Our method is not only competitive with other LLM-based methods but is also more scalable for real-world POI recommender systems. Results demonstrate that our approach consistently outperforms baseline methods, offering a more interpretable and resource-efficient solution for POI recommendation systems. Our source code is available at: https://github.com/w11wo/GenUP/.