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.

通过输入增强措施，我们研究了各种提示策略来提高大型语言模型在个性化内容推荐中的性能。我们提出了一种名为 LLM-Rec 的方法，包括四种不同的提示策略：基本提示、推荐驱动的提示、参与驱动的提示和推荐驱动加参与驱动的提示。我们的实证实验证明结合原始内容描述和 LLM 生成的增强输入文本，使用这些提示策略可以改善推荐性能。这一发现强调了将多样的提示和输入增强技术与大型语言模型相结合以提高个性化内容推荐能力的重要性。