Recommender systems (RSs) play a pervasive role in today's online services, yet their closed-loop nature constrains their access to open-world knowledge. Recently, large language models (LLMs) have shown promise in bridging this gap. However, previous attempts to directly implement LLMs as recommenders fall short in meeting the requirements of industrial RSs, particularly in terms of online inference latency and offline resource efficiency. Thus, we propose REKI to acquire two types of external knowledge about users and items from LLMs. Specifically, we introduce factorization prompting to elicit accurate knowledge reasoning on user preferences and items. We develop individual knowledge extraction and collective knowledge extraction tailored for different scales of scenarios, effectively reducing offline resource consumption. Subsequently, generated knowledge undergoes efficient transformation and condensation into augmented vectors through a hybridized expert-integrated network, ensuring compatibility. The obtained vectors can then be used to enhance any conventional recommendation model. We also ensure efficient inference by preprocessing and prestoring the knowledge from LLMs. Experiments demonstrate that REKI outperforms state-of-the-art baselines and is compatible with lots of recommendation algorithms and tasks. Now, REKI has been deployed to Huawei's news and music recommendation platforms and gained a 7% and 1.99% improvement during the online A/B test.

翻译：推荐系统在当今在线服务中扮演着普遍角色，但其闭环特性限制了其对开放世界知识的获取。近年来，大语言模型展现出弥合这一差距的潜力。然而，先前直接使用大语言模型作为推荐器的尝试未能满足工业级推荐系统的要求，特别是在在线推理延迟和离线资源效率方面。为此，我们提出REKI框架，旨在从大语言模型中获取关于用户和物品的两类外部知识。具体而言，我们引入因子化提示技术以激发对用户偏好和物品的精准知识推理。针对不同规模场景，我们分别设计了个体知识提取与集体知识提取方法，有效降低了离线资源消耗。随后，通过专家混合集成网络将生成的知识高效转化并压缩为增强向量，确保兼容性。所得向量可用于增强任何传统推荐模型。我们还通过预处理和预存储大语言模型生成的知识来保障高效推理。实验表明，REKI在性能上优于现有最先进基线，且与多种推荐算法及任务兼容。目前，REKI已部署于华为新闻与音乐推荐平台，在线A/B测试中分别获得7%与1.99%的性能提升。