Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model's interpretability and accuracy. This paper introduces an end-to-end deep learning model, named RKGCN, which dynamically analyses each user's preferences and makes a recommendation of suitable items. It combines knowledge graphs on both the item side and user side to enrich their representations to maximize the utilization of the abundant information in knowledge graphs. RKGCN is able to offer more personalized and relevant recommendations in three different scenarios. The experimental results show the superior effectiveness of our model over 5 baseline models on three real-world datasets including movies, books, and music.

翻译：利用知识图谱辅助深度学习模型进行推荐决策，已被证明能有效提升模型的可解释性和准确性。本文提出一种名为RKGCN的端到端深度学习模型，该模型可动态分析每位用户的偏好并推荐合适物品。通过融合物品侧与用户侧的知识图谱，模型增强了双方表征能力，从而最大化利用知识图谱中的丰富信息。RKGCN能在三种不同场景下提供更具个性化和相关性的推荐。实验结果表明，在包括电影、书籍和音乐在内的三个真实数据集上，本模型相较于5个基线模型展现出显著优越性。