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发挥着关键作用。然而，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（LLM）的出现因能编码广泛的世界知识并展现推理能力，为弥合这一鸿沟带来了希望。尽管如此，此前直接以LLM作为推荐器的尝试并未取得令人满意的效果。本文提出基于大语言模型的开放世界知识增强推荐框架（KAR），旨在从LLM中获取两类外部知识——关于用户偏好的推理知识和关于物品的事实知识。我们引入因式分解提示方法以精准激发对用户偏好的推理。通过混合专家适配器，生成的推理知识及事实知识被有效转换并压缩为增强向量，从而适配推荐任务。所得向量可直接用于提升任意推荐模型的性能。同时，我们采用预处理与预存储LLM知识的方式确保高效推理。大量实验表明，KAR显著优于各最先进基线方法，并能与多种推荐算法兼容。