Li, C.Cao, Y.Zhu, Y.Cheng, D.Li, C.Morimoto, Y.2025-12-182025-12-182024Machine Intelligence Research, 2024; 21(3):481-4942731-538X2731-5398https://hdl.handle.net/11541.2/37579Using 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 representation-enhanced knowledge graph convolutional networks (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.enCopyright 2024 The Authors (https://link.springer.com/journal/11633) Access Condition Notes: Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.deep learninggraph convolutional networks (GCNs)graph neural networks (GNNs)knowledge graphrecommendation systemsJournal article10.1007/s11633-023-1440-x2-s2.0-85183025760