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.

翻译：推荐系统在各类在线服务中扮演着至关重要的角色。然而，传统推荐系统在特定领域内独立训练与部署的封闭特性，限制了其对开放世界知识的获取。近期，大语言模型（LLMs）的兴起通过编码广泛的世界知识并展现推理能力，展现出弥合这一鸿沟的潜力。尽管如此，先前直接使用LLMs作为推荐器的尝试未能取得令人满意的效果。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——关于用户偏好的推理知识与关于物品的事实知识。我们引入分解式提示方法以引发对用户偏好的精准推理。通过混合专家适配器将生成的推理知识与事实知识有效转化并压缩为增强向量，使其与推荐任务相兼容。所得向量可直接用于增强任意推荐模型的性能。同时，我们通过预处理与预存储LLMs生成的知识，确保推理的高效性。大量实验表明，KAR显著优于当前最先进的基线方法，并与多种推荐算法兼容。