In large language models (LLM)-based recommendation systems (LLM-RSs), accurately predicting user preferences by leveraging the general knowledge of LLMs is possible without requiring extensive training data. By converting recommendation tasks into natural language inputs called prompts, LLM-RSs can efficiently solve issues that have been difficult to address due to data scarcity but are crucial in applications such as cold-start and cross-domain problems. However, when applying this in practice, selecting the prompt that matches tasks and data is essential. Although numerous prompts have been proposed in LLM-RSs and representing the target user in prompts significantly impacts recommendation accuracy, there are still no clear guidelines for selecting specific prompts. In this paper, we categorize and analyze prompts from previous research to establish practical prompt selection guidelines. Through 450 experiments with 90 prompts and five real-world datasets, we examined the relationship between prompts and dataset characteristics in recommendation accuracy. We found that no single prompt consistently outperforms others; thus, selecting prompts on the basis of dataset characteristics is crucial. Here, we propose a prompt selection method that achieves higher accuracy with minimal validation data. Because increasing the number of prompts to explore raises costs, we also introduce a cost-efficient strategy using high-performance and cost-efficient LLMs, significantly reducing exploration costs while maintaining high prediction accuracy. Our work offers valuable insights into the prompt selection, advancing accurate and efficient LLM-RSs.

翻译：在基于大型语言模型（LLM）的推荐系统（LLM-RS）中，无需大量训练数据即可利用LLM的通用知识准确预测用户偏好。通过将推荐任务转化为称为提示词的自然语言输入，LLM-RS能够有效解决因数据稀缺而难以处理但对冷启动和跨领域等应用场景至关重要的问题。然而，在实际应用中，选择与任务及数据相匹配的提示词至关重要。尽管LLM-RS领域已提出众多提示词构建方法，且提示词中对目标用户的表征方式会显著影响推荐精度，但目前仍缺乏选择具体提示词的明确指导原则。本文通过对现有研究中的提示词进行分类分析，建立了实用的提示词选择指南。通过使用90种提示词在五个真实数据集上进行的450项实验，我们系统考察了提示词与数据集特征对推荐精度的关联规律。研究发现，不存在始终优于其他方案的单一提示词，因此基于数据集特征选择提示词至关重要。为此，我们提出一种能够以最少验证数据实现更高精度的提示词选择方法。鉴于增加待探索提示词数量会提升成本，我们还引入了一种采用高性能且成本效益优化的LLM的经济策略，该策略在保持高预测精度的同时显著降低了探索成本。本研究为提示词选择提供了重要见解，推动了准确高效的LLM-RS发展。