Ripple Knowledge Graph Convolutional Networks for Recommendation Systems  

作　　者：Chen Li Yang Cao Ye Zhu Debo Cheng Chengyuan Li Yasuhiko Morimoto 

机构地区：[1]Graduate School of Informatics,Nagoya University,Chikusa,Nagoya 464-8602,Japan [2]Centre for Cyber Resilience and Trust,Deakin University,Burwood 3125,Australia [3]Science,Technology,Engineering and Mathematics(STEM),University of South Australia,Adelaide 5000,Australia [4]Graduate School of Engineering,Hiroshima University,Higashi-hiroshima 10587,Japan

出　　处：《Machine Intelligence Research》2024年第3期481-494,共14页机器智能研究（英文版）

摘　　要：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 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.

关 键 词：Deep learning recommendation systems knowledge graph graph convolutional networks(GCNs) graph neural networks(GNNs) 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]