标题： 平衡微调与RAG：一种动态LLM推荐更新的混合策略 显示英文标题

标题： Balancing Fine-tuning and RAG: A Hybrid Strategy for Dynamic LLM Recommendation Updates

摘要： 大型语言模型（LLMs）通过其先进的推理和规划能力增强了推荐系统。 然而，用户兴趣和内容的动态性带来了重大挑战：虽然初始微调使LLMs与领域知识和用户偏好对齐，但它无法捕捉这种实时变化，因此需要强大的更新机制。 本文研究了更新LLM驱动推荐系统的方法，重点在于持续微调与检索增强生成（RAG）之间的权衡。 以一个LLM驱动的用户兴趣探索系统为例，我们在成本、敏捷性和知识整合等维度上对这些方法进行了比较分析。 我们提出了一种混合更新策略，结合定期微调的长期知识适应性与低成本RAG的敏捷性。 我们通过在一个十亿用户平台上进行的实时A/B实验证明，这种混合方法在用户满意度方面产生了统计学上的显著提升，为维护高质量的LLM驱动推荐系统提供了一个实用且经济的框架。 显示英文摘要

摘要： 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.