Recommender systems play a vital role in various online services. However, the insulated nature of training and deploying separately within a specific domain limits their access to open-world knowledge. Recently, the emergence of large language models (LLMs) has shown promise in bridging this gap by encoding extensive world knowledge and demonstrating reasoning capability. Nevertheless, previous attempts to directly use LLMs as recommenders have not achieved satisfactory results. In this work, we propose an Open-World Knowledge Augmented Recommendation Framework with Large Language Models, dubbed KAR, to acquire two types of external knowledge from LLMs -- the reasoning knowledge on user preferences and the factual knowledge on items. We introduce factorization prompting to elicit accurate reasoning on user preferences. The generated reasoning and factual knowledge are effectively transformed and condensed into augmented vectors by a hybrid-expert adaptor in order to be compatible with the recommendation task. The obtained vectors can then be directly used to enhance the performance of any recommendation model. We also ensure efficient inference by preprocessing and prestoring the knowledge from the LLM. Extensive experiments show that KAR significantly outperforms the state-of-the-art baselines and is compatible with a wide range of recommendation algorithms. We deploy KAR to Huawei's news and music recommendation platforms and gain a 7\% and 1.7\% improvement in the online A/B test, respectively.

翻译：推荐系统在各种在线服务中扮演着重要角色。然而，其在特定领域内单独训练和部署的封闭特性限制了其对开放世界知识的获取。近期，大型语言模型（LLMs）的崛起展现出弥补这一差距的潜力，它们编码了广泛的世界知识并具备推理能力。尽管如此，此前直接将LLMs用作推荐器的尝试未能取得令人满意的结果。本文提出一种基于大语言模型的开放世界知识增强推荐框架KAR，从LLMs中获取两类外部知识——用户偏好的推理知识和物品的事实知识。我们引入分解提示技术以激发对用户偏好的精准推理。通过混合专家适配器，生成的推理知识和事实知识被高效转换并压缩为增强向量，从而与推荐任务兼容。获得的增强向量可直接用于提升任意推荐模型的性能。此外，我们通过预处理和预存储LLM中的知识来保证高效的推理效率。大量实验表明，KAR显著优于当前最先进的基准模型，并兼容多种推荐算法。我们已在华为的新闻和音乐推荐平台部署KAR，在线A/B测试分别取得7%和1.7%的性能提升。