PathGPT: Reframing Path Recommendation as a Natural Language Generation Task with Retrieval-Augmented Language Models
This addresses the need for flexible and generalizable path recommendation systems that avoid costly retraining, benefiting users in travel and logistics domains, though it is incremental as it integrates existing retrieval and generative components.
The paper tackles the problem of generating personalized travel paths by reframing path recommendation as a natural language generation task, using a retrieval-augmented LLM system called PathGPT, which achieves competitive performance with specialized methods on large-scale datasets.
Path recommendation (PR) aims to generate travel paths that are customized to a user's specific preferences and constraints. Conventional approaches often employ explicit optimization objectives or specialized machine learning architectures; however, these methods typically exhibit limited flexibility and generalizability, necessitating costly retraining to accommodate new scenarios. This paper introduces an alternative paradigm that conceptualizes PR as a natural language generation task. We present PathGPT, a retrieval-augmented large language model (LLM) system that leverages historical trajectory data and natural language user constraints to generate plausible paths. The proposed methodology first converts raw trajectory data into a human-interpretable textual format, which is then stored in a database. Subsequently, a hybrid retrieval system extracts path-specific context from this database to inform a pretrained LLM. The primary contribution of this work is a novel framework that demonstrates how integrating established information retrieval and generative model components can enable adaptive, zero-shot path generation across diverse scenarios. Extensive experiments on large-scale trajectory datasets indicate that PathGPT's performance is competitive with specialized, learning-based methods, underscoring its potential as a flexible and generalizable path generation system that avoids the need for retraining inherent in previous data-driven models.