首页|Ripple Knowledge Graph Convolutional Networks for Recommendation Systems

Ripple Knowledge Graph Convolutional Networks for Recommendation Systems

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to ef-fectively improve the model's interpretability and accuracy.This paper introduces an end-to-end deep learning model,named represent-ation-enhanced knowledge graph convolutional networks(RKGCN),which dynamically analyses each user's preferences and makes a re-commendation 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 re-commendations in three different scenarios.The experimental results show the superior effectiveness of our model over 5 baseline mod-els on three real-world datasets including movies,books,and music.

Deep learningrecommendation systemsknowledge graphgraph convolutional networks(GCNs)graph neural networks(GNNs)

Chen Li、Yang Cao、Ye Zhu、Debo Cheng、Chengyuan Li、Yasuhiko Morimoto

展开 >

Graduate School of Informatics,Nagoya University,Chikusa,Nagoya 464-8602,Japan

Centre for Cyber Resilience and Trust,Deakin University,Burwood 3125,Australia

Science,Technology,Engineering and Mathematics(STEM),University of South Australia,Adelaide 5000,Australia

Graduate School of Engineering,Hiroshima University,Higashi-hiroshima 10587,Japan

展开 >

Open Access funding enabled and organized by CAUL

2024

10.1007/s11633-023-1440-x

机器智能研究(英文)
中国科学院自动化所

机器智能研究(英文)

CSTPCDEI
影响因子：0.49
ISSN：2731-538X
年,卷(期)：2024.21(3)
  • 45