基于实用大语言模型的下一步POI预测的演示选择比较研究
A Comparative Study of Demonstration Selection for Practical Large Language Models-based Next POI Prediction
AI总结：
本文比较了基于大语言模型的POI预测中演示选择策略，评估了启发式方法在计算成本和预测精度上的优势。
摘要：
本文研究了利用大语言模型（LLMs）预测用户下一步兴趣点（POI）的演示选择策略，旨在基于历史签到数据准确预测用户后续位置。尽管上下文学习（ICL）近期作为传统监督方法的有希望替代方案受到关注，但ICL的效果显著依赖于所选演示。尽管先前研究已考察了随机选择、基于嵌入的选择和任务特定选择等方法，但这些策略之间的全面比较仍存在不足。为填补这一空白并明确实际应用的最佳实践，我们综合评估了现有演示选择方法以及更简单的启发式方法，如地理接近性、时间顺序和序列模式。在三个真实世界数据集上进行的广泛实验表明，这些启发式方法在计算成本和预测准确性方面均优于更复杂且计算需求更高的嵌入方法。值得注意的是，在某些场景中，使用这些简单启发式方法选择的演示的LLM甚至能超越现有微调模型，而无需进一步训练。我们的源代码可在：https://github.com/ryonsd/DS-LLM4POI获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
