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.

本文提出了双向推荐模型 DEKGCI，利用用户物品交互图的高阶协作信号丰富用户表示，同时利用知识图谱的高阶结构和语义信息丰富物品表示，实现了用户和物品之间的有效互动捕捉。在三个真实数据集上的实验结果表明，DEKGCI 相对于七种最先进的基线模型在 AUC 和 ACC 方面具有高效性。