LLM-Rec：通过大型语言模型提示进行个性化推荐

摘要

我们研究了各种提示策略，通过输入增强来提高大型语言模型（LLMs）在个性化内容推荐性能方面的表现。我们提出的方法称为LLM-Rec，包括四种不同的提示策略：（1）基本提示，（2）推荐驱动提示，（3）参与引导提示，以及（4）推荐驱动+参与引导提示。我们的实证实验表明，将原始内容描述与LLM生成的增强输入文本结合起来，使用这些提示策略可以提高推荐性能。这一发现突显了结合多样的提示和输入增强技术以提升大型语言模型在个性化内容推荐中的推荐能力的重要性。

English

We investigate various prompting strategies for enhancing personalized content recommendation performance with large language models (LLMs) through input augmentation. Our proposed approach, termed LLM-Rec, encompasses four distinct prompting strategies: (1) basic prompting, (2) recommendation-driven prompting, (3) engagement-guided prompting, and (4) recommendation-driven + engagement-guided prompting. Our empirical experiments show that combining the original content description with the augmented input text generated by LLM using these prompting strategies leads to improved recommendation performance. This finding highlights the importance of incorporating diverse prompts and input augmentation techniques to enhance the recommendation capabilities with large language models for personalized content recommendation.

LLM-Rec：通过大型语言模型提示进行个性化推荐

LLM-Rec: Personalized Recommendation via Prompting Large Language Models

摘要

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