[2604.06207] A Comparative Study of Demonstration Selection for Practical Large Language Models-based Next POI Prediction

Computer Science > Computation and Language arXiv:2604.06207 (cs) [Submitted on 16 Mar 2026] Title:A Comparative Study of Demonstration Selection for Practical Large Language Models-based Next POI Prediction Authors:Ryo Nishida, Masayuki Kawarada, Tatsuya Ishigaki, Hiroya Takamura, Masaki Onishi View a PDF of the paper titled A Comparative Study of Demonstration Selection for Practical Large Language Models-based Next POI Prediction, by Ryo Nishida and Masayuki Kawarada and Tatsuya Ishigaki and Hiroya Takamura and Masaki Onishi View PDF HTML (experimental) Abstract:This paper investigates demonstration selection strategies for predicting a user's next point-of-interest (POI) using large language models (LLMs), aiming to accurately forecast a user's subsequent location based on historical check-in data. While in-context learning (ICL) with LLMs has recently gained attention as a promising alternative to traditional supervised approaches, the effectiveness of ICL significantly depends on the selected demonstration. Although previous studies have examined methods such as random selection, embedding-based selection, and task-specific selection, there remains a lack of comprehensive comparative analysis among these strategies. To bridge this gap and clarify the best practices for real-world applications, we comprehensively evaluate existing demonstration selection methods alongside simpler heuristic approaches such as geographical proximity, temporal ordering, and sequential pa...