用大模型生成可解释推荐，让系统既准又懂人
ReasoningRec: Bridging Personalized Recommendations and Human-Interpretable Explanations through LLM Reasoning
- 用大模型分析用户偏好与反感，生成推理过程
- 小模型经大模型生成解释微调后，推荐准确率提升12.5%
- 适合需要透明推荐的场景，如医疗、金融
本文提出 ReasoningRec，一种基于大语言模型（LLM）的推荐框架，旨在弥合推荐结果与人类可理解解释之间的鸿沟。不同于依赖隐式用户-物品交互的传统推荐系统，ReasoningRec 利用大模型建模用户和物品，聚焦于偏好、厌恶及解释性推理。该框架使用大模型生成用户偏好的合成解释，再用于微调小型模型，以提升推荐准确率与解释可读性。实验研究了推理与上下文信息对个性化推荐的影响，发现上下文与个性化数据质量显著影响大模型生成合理解释的能力。实证评估表明，ReasoningRec 在推荐预测上超越现有方法最高达12.5%，同时提供人类可理解的解释。代码已开源：https://github.com/millenniumbismay/reasoningrec。
原文摘要 · Abstract (English)
This paper presents ReasoningRec, a reasoning-based recommendation framework that leverages Large Language Models (LLMs) to bridge the gap between recommendations and human-interpretable explanations. In contrast to conventional recommendation systems that rely on implicit user-item interactions, ReasoningRec employs LLMs to model users and items, focusing on preferences, aversions, and explanatory reasoning. The framework utilizes a larger LLM to generate synthetic explanations for user preferences, subsequently used to fine-tune a smaller LLM for enhanced recommendation accuracy and human-interpretable explanation. Our experimental study investigates the impact of reasoning and contextual information on personalized recommendations, revealing that the quality of contextual and personalized data significantly influences the LLM's capacity to generate plausible explanations. Empirical evaluations demonstrate that ReasoningRec surpasses state-of-the-art methods by up to 12.5\% in recommendation prediction while concurrently providing human-intelligible explanations. The code is available here: https://github.com/millenniumbismay/reasoningrec.
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