  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.
