图神经网络增强交互协同过滤推荐算法OA
Graph-enhanced neural interactive collaborative filtering
为提升冷启动场景下交互推荐系统的训练效率和推荐精度,基于一个公开数据集的真实数据,根据用户交互构建了一种商品相似度连接图,并设计了基于深度强化学习的图神经网络增强交互协同过滤模型(GE-ICF)来进行仿真实验.该模型基于深度强化学习框架,采用图神经网络进行向量传播层设计,在商品相似度连接图中挖掘商品间关系,优化商品向量准确度.结果表明:在冷启动交互推荐场景下,商品相似度连接图能够对大规模稀疏交互推荐数据关系进行高效建模,有效提升训练效率;GE-I…查看全部>>
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…查看全部>>
谢程燕;董璐
东南大学自动化学院,南京210096东南大学网络空间安全学院,南京211189
信息技术与安全科学
交互推荐系统冷启动图神经网络深度强化学习
interactive recommendation systemscold-startgraph neural networkdeep reinforcement learning
《东南大学学报（英文版）》 2022 (2)
基于迁移强化学习的多自主体博弈策略研究
110-117,8
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.
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