Ripple Knowledge Graph Convolutional Networks For Recommendation Systems
This work addresses the need for more accurate and interpretable recommendations in domains like entertainment, though it appears incremental as it builds on existing knowledge graph methods.
The paper tackled the problem of improving recommendation systems by integrating knowledge graphs on both user and item sides to enhance personalization and relevance. The proposed RKGCN model demonstrated superior effectiveness over 5 baseline models across three real-world datasets in movies, books, and music.
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.