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 \underline{Re}flective
\underline{Re}inforcement \underline{L}arge \underline{L}anguage
\underline{M}odel (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.

提出了一种反思性强化大语言模型（Reflective Reinforcement Large Language Model，Re2LLM）用于会话推荐，通过构建知识库来引导大语言模型（LLM）关注专业知识，以实现更准确的推荐。