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.

翻译：我们研究了通过输入增强来提升大语言模型（LLMs）在个性化内容推荐中性能的多种提示策略。所提出的方法名为LLM-Rec，包含四种不同的提示策略：（1）基础提示、（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使用这些提示策略生成的增强输入文本相结合，能够改善推荐性能。这一发现强调了融合多样化提示与输入增强技术以利用大语言模型增强个性化内容推荐能力的重要性。