Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performance. Nevertheless, the knowledge graphs used in previous work, namely metadata-based knowledge graphs, are usually constructed based on the attributes of items and co-occurring relations (e.g., also buy), in which the former provides limited information and the latter relies on sufficient interaction data and still suffers from cold start issue. Common sense, as a form of knowledge with generality and universality, can be used as a supplement to the metadata-based knowledge graph and provides a new perspective for modeling users' preferences. Recently, benefiting from the emergent world knowledge of the large language model, efficient acquisition of common sense has become possible. In this paper, we propose a novel knowledge-based recommendation framework incorporating common sense, CSRec, which can be flexibly coupled to existing knowledge-based methods. Considering the challenge of the knowledge gap between the common sense-based knowledge graph and metadata-based knowledge graph, we propose a knowledge fusion approach based on mutual information maximization theory. Experimental results on public datasets demonstrate that our approach significantly improves the performance of existing knowledge-based recommendation models.

中文翻译：

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基于大语言模型的常识增强型知识推荐

基于知识的推荐模型利用知识图谱中的边信息有效缓解了数据稀疏问题，并取得了可观的性能。然而，之前的工作中使用的知识图谱，即基于元数据的知识图谱，通常是基于项目的属性和共现关系（例如，也购买）构建的，其中前者提供有限的信息，后者依赖于交互数据充足，但仍然存在冷启动问题。常识作为一种具有普遍性和普遍性的知识形式，可以作为基于元数据的知识图谱的补充，为建模用户偏好提供了新的视角。最近，受益于大语言模型的新兴世界知识，有效获取常识已成为可能。在本文中，我们提出了一种结合常识的新颖的基于知识的推荐框架CSRec，它可以灵活地与现有的基于知识的方法耦合。考虑到基于常识的知识图谱和基于元数据的知识图谱之间知识鸿沟的挑战，我们提出了一种基于互信息最大化理论的知识融合方法。公共数据集上的实验结果表明，我们的方法显着提高了现有基于知识的推荐模型的性能。