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

• URL: http://arxiv.org/abs/2604.06207v1
• Date: Mon, 16 Mar 2026 03:34:22 GMT
• Title: A Comparative Study of Demonstration Selection for Practical Large Language Models-based Next POI Prediction
• Abstract summary: In-context learning (ICL) with large language models (LLMs) has recently gained attention as a promising alternative to traditional supervised approaches.<n>This paper investigates demonstration selection strategies for predicting a user's next point-of-interest (POI) using large language models (LLMs)
• Score: 15.310992304536626
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 patterns. Extensive experiments conducted on three real-world datasets indicate that these heuristic methods consistently outperform more complex and computationally demanding embedding-based methods, both in terms of computational cost and prediction accuracy. Notably, in certain scenarios, LLMs using demonstrations selected by these simpler heuristic methods even outperform existing fine-tuned models, without requiring further training. Our source code is available at: https://github.com/ryonsd/DS-LLM4POI.

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