Recently, large language models such as ChatGPT have showcased remarkable abilities in solving general tasks, demonstrating the potential for applications in recommender systems. To assess how effectively LLMs can be used in recommendation tasks, our study primarily focuses on employing LLMs as recommender systems through prompting engineering. We propose a general framework for utilizing LLMs in recommendation tasks, focusing on the capabilities of LLMs as recommenders. To conduct our analysis, we formalize the input of LLMs for recommendation into natural language prompts with two key aspects, and explain how our framework can be generalized to various recommendation scenarios. As for the use of LLMs as recommenders, we analyze the impact of public availability, tuning strategies, model architecture, parameter scale, and context length on recommendation results based on the classification of LLMs. As for prompt engineering, we further analyze the impact of four important components of prompts, \ie task descriptions, user interest modeling, candidate items construction and prompting strategies. In each section, we first define and categorize concepts in line with the existing literature. Then, we propose inspiring research questions followed by experiments to systematically analyze the impact of different factors on two public datasets. Finally, we summarize promising directions to shed lights on future research.

翻译：近期，以ChatGPT为代表的大语言模型在通用任务中展现出卓越能力，揭示了其在推荐系统中的潜在应用价值。为评估大语言模型在推荐任务中的有效性，本研究主要通过提示工程将其应用于推荐系统。我们提出了一个面向推荐任务的大语言模型通用框架，重点分析了其作为推荐器的核心能力。在分析过程中，我们将大语言模型在推荐中的输入形式化为包含两个关键维度的自然语言提示，并阐释了该框架向不同推荐场景的泛化机制。针对大语言模型作为推荐器的应用，我们基于其分类体系，系统分析了模型公开可用性、调优策略、架构类型、参数量级及上下文长度对推荐结果的影响。在提示工程方面，进一步解析了任务描述、用户兴趣建模、候选项目构建及提示策略这四个关键组件的效应。每个研究模块中，我们首先依据现有文献对相关概念进行界定与分类，继而提出启发性研究问题并设计实验，在两个公开数据集上系统剖析不同因素的交互影响。最后，我们总结了未来研究的若干重要方向。