Inspired by the exceptional general intelligence of Large Language Models
(LLMs), researchers have begun to explore their application in pioneering the
next generation of recommender systems - systems that are conversational,
explainable, and controllable. However, existing literature primarily
concentrates on integrating domain-specific knowledge into LLMs to enhance
accuracy, often neglecting the ability to follow instructions. To address this
gap, we initially introduce a collection of supervised learning tasks,
augmented with labels derived from a conventional recommender model, aimed at
explicitly improving LLMs' proficiency in adhering to recommendation-specific
instructions. Subsequently, we develop a reinforcement learning-based alignment
procedure to further strengthen LLMs' aptitude in responding to users'
intentions and mitigating formatting errors. Through extensive experiments on
two real-world datasets, our method markedly advances the capability of LLMs to
comply with instructions within recommender systems, while sustaining a high
level of accuracy performance.

通过引入监督学习任务和基于强化学习的对齐过程，本研究明显提升了大型语言模型在推荐系统中遵循指示并保持高精度性能的能力。