Both knowledge graphs and user-item interaction graphs are frequently used in recommender systems due to their ability to provide rich information for modeling users and items. However, existing studies often focused on one of these sources (either the knowledge graph or the user-item interaction graph), resulting in underutilization of the benefits that can be obtained by integrating both sources of information. In this paper, we propose DEKGCI, a novel double-sided recommendation model. In DEKGCI, we use the high-order collaborative signals from the user-item interaction graph to enrich the user representations on the user side. Additionally, we utilize the high-order structural and semantic information from the knowledge graph to enrich the item representations on the item side. DEKGCI simultaneously learns the user and item representations to effectively capture the joint interactions between users and items. Three real-world datasets are adopted in the experiments to evaluate DEKGCI's performance, and experimental results demonstrate its high effectiveness compared to seven state-of-the-art baselines in terms of AUC and ACC.

翻译：知识图谱和用户-物品交互图均因能为建模用户和物品提供丰富信息而频繁用于推荐系统中。然而，现有研究往往聚焦于其中一种数据源（知识图谱或用户-物品交互图），导致未能充分利用整合两种信息源所能带来的优势。本文提出DEKGCI，一种新颖的双侧推荐模型。在DEKGCI中，我们利用来自用户-物品交互图的高阶协同信号来丰富用户侧的用户表示。同时，我们利用来自知识图谱的高阶结构与语义信息来丰富物品侧的物品表示。DEKGCI同步学习用户与物品表示，以有效捕捉用户与物品之间的联合交互。实验采用三个真实世界数据集评估DEKGCI的性能，结果表明，在AUC和ACC指标上，DEKGCI相较于七种最先进基准模型具有显著优势。