混合微调与检索增强，实现推荐系统动态更新
Balancing Fine-tuning and RAG: A Hybrid Strategy for Dynamic LLM Recommendation Updates
- 结合周期性微调的长期知识学习与RAG的快速响应
- 在十亿用户平台实测，显著提升用户满意度
- 适合需要实时更新且成本敏感的推荐场景
大型语言模型（LLMs）通过其强大的推理与规划能力赋能推荐系统。然而，用户兴趣和内容的动态变化带来了挑战：初始微调虽能对齐领域知识与用户偏好，却难以捕捉实时变化，亟需高效更新机制。本文以基于LLM的用户兴趣探索系统为例，对比分析持续微调与检索增强生成（RAG）在成本、敏捷性及知识融入方面的差异。提出一种混合更新策略，融合周期性微调的长期适应性与低成本RAG的敏捷优势。在十亿用户平台上开展真实A/B实验，结果表明该策略在用户满意度上具有统计显著提升，为维护高质量LLM驱动的推荐系统提供了实用且经济的框架。
原文摘要 · Abstract (English)
Large Language Models (LLMs) empower recommendation systems through their advanced reasoning and planning capabilities. However, the dynamic nature of user interests and content poses a significant challenge: While initial fine-tuning aligns LLMs with domain knowledge and user preferences, it fails to capture such real-time changes, necessitating robust update mechanisms. This paper investigates strategies for updating LLM-powered recommenders, focusing on the trade-offs between ongoing fine-tuning and Retrieval-Augmented Generation (RAG). Using an LLM-powered user interest exploration system as a case study, we perform a comparative analysis of these methods across dimensions like cost, agility, and knowledge incorporation. We propose a hybrid update strategy that leverages the long-term knowledge adaptation of periodic fine-tuning with the agility of low-cost RAG. We demonstrate through live A/B experiments on a billion-user platform that this hybrid approach yields statistically significant improvements in user satisfaction, offering a practical and cost-effective framework for maintaining high-quality LLM-powered recommender systems.
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