  Large Language Models (LLMs) are emerging as promising approaches to enhance
session-based recommendation (SBR), where both prompt-based and
fine-tuning-based methods have been widely investigated to align LLMs with SBR.
However, the former methods struggle with optimal prompts to elicit the correct
reasoning of LLMs due to the lack of task-specific feedback, leading to
unsatisfactory recommendations. Although the latter methods attempt to
fine-tune LLMs with domain-specific knowledge, they face limitations such as
high computational costs and reliance on open-source backbones. To address such
issues, we propose a Reflective Reinforcement Large Language Model (Re2LLM) for
SBR, guiding LLMs to focus on specialized knowledge essential for more accurate
recommendations effectively and efficiently. In particular, we first design the
Reflective Exploration Module to effectively extract knowledge that is readily
understandable and digestible by LLMs. To be specific, we direct LLMs to
examine recommendation errors through self-reflection and construct a knowledge
base (KB) comprising hints capable of rectifying these errors. To efficiently
elicit the correct reasoning of LLMs, we further devise the Reinforcement
Utilization Module to train a lightweight retrieval agent. It learns to select
hints from the constructed KB based on the task-specific feedback, where the
hints can serve as guidance to help correct LLMs reasoning for better
recommendations. Extensive experiments on multiple real-world datasets
demonstrate that our method consistently outperforms state-of-the-art methods.
