东南大学学报（英文版）2022，Vol.38Issue(2)：110-117,8.DOI:10.3969/j.issn.1003-7985.2022.02.002
图神经网络增强交互协同过滤推荐算法
Graph-enhanced neural interactive collaborative filtering
摘要
Abstract
To improve the training efficiency and recommendation accuracy in cold-start interactive recommendation systems,a new graph structure called item similarity graph is proposed on the basis of real data from a public dataset.The proposed graph is built from collaborative interactions and a deep reinforcement learning-based graph-enhanced neural interactive collaborative filtering(GE-ICF)model.The GE-ICF framework is developed with a deep reinforcement learning framework and comprises an embedding propagation layer designed with graph neural networks.Extensive experiments are conducted to investigate the efficiency of the proposed graph structure and the superiority of the proposed GE-ICF framework.Results show that in cold-start interactive recommendation systems,the proposed item similarity graph performs well in data relationship modeling,with the training efficiency showing significant improvement.The proposed GE-ICF framework also demonstrates superiority in decision modeling,thereby increasing the recommendation accuracy remarkably.关键词
交互推荐系统/冷启动/图神经网络/深度强化学习Key words
interactive recommendation systems/cold-start/graph neural network/deep reinforcement learning分类
信息技术与安全科学引用本文复制引用
谢程燕,董璐..图神经网络增强交互协同过滤推荐算法[J].东南大学学报（英文版）,2022,38(2):110-117,8.基金项目
The National Natural Science Foundation of China(No.62173251),the Guangdong Provincial Key Laboratory of Intelli-gent Decision and Cooperative Control,the Fundamental Research Funds for the Central Universities. （No.62173251）
