  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.
