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

Ripple Knowledge Graph Convolutional Networks For Recommendation Systems