We investigate various prompting strategies for enhancing personalized 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 incorporating the augmented input text generated by LLM leads to improved recommendation performance. Recommendation-driven and engagement-guided prompting strategies are found to elicit LLM's understanding of global and local item characteristics. This finding highlights the importance of leveraging diverse prompts and input augmentation techniques to enhance the recommendation capabilities with LLMs.

翻译：我们研究了多种提示策略，通过输入增强来提升大语言模型（LLM）的个性化推荐性能。所提出的方法称为LLM-Rec，包含四种不同的提示策略：（1）基础提示法、（2）推荐驱动提示法、（3）参与度引导提示法以及（4）推荐驱动+参与度引导联合提示法。实验结果表明，融入LLM生成的增强输入文本能有效提升推荐性能。研究发现，推荐驱动与参与度引导的提示策略能够激发LLM对全局与局部商品特征的理解。这一发现凸显了利用多样化提示与输入增强技术来强化LLM推荐能力的重要性。