Recommender systems have attracted widespread attention in various online applications. To effectively recommend the needed items of users, knowledge graphs have been introduced to provide rich and complementary information to infer user preferences in recommender systems. Existing efforts have explored user preferences through specific paths and item embedding in knowledge graphs. However, user preferences have hardly been fully captured because users and items are always separately modeled. To address this problem, we propose a model to represent items from a user’s perspective that provides effective supplementary information. User preferences encoded in historically clicked items are propagated along links in the knowledge graph. We propose a gated attention unit to capture user preferences from specific types of paths. Based on the captured preference information through the knowledge graph and supplementary item information, we generate effective reasoning paths to infer the underlying rationale of user–item interactions using the sequential model. Through extensive experiments on real-world datasets, we demonstrate that the proposed model achieves significant improvements over the state-of-the-art solutions.

推荐系统在各种在线应用中引起了广泛关注。为了有效地推荐用户所需的项目，引入了知识图来提供丰富的补充信息来推断推荐系统中的用户偏好。现有的努力已经通过特定路径和项目嵌入知识图中来探索用户偏好。然而，用户偏好几乎没有被完全捕获，因为用户和项目总是分开建模。为了解决这个问题，我们提出了一个模型来从用户的角度表示项目，提供有效的补充信息。在历史点击项目中编码的用户偏好沿着知识图中的链接传播。我们提出了一个门控注意单元来从特定类型的路径中捕获用户偏好。基于通过知识图和补充项目信息捕获的偏好信息，我们生成有效的推理路径，以使用序列模型推断用户-项目交互的基本原理。通过对真实世界数据集的大量实验，我们证明所提出的模型比最先进的解决方案取得了显着的改进。