计算技术与自动化2024，Vol.43Issue(4)：73-78,6.DOI:10.16339/j.cnki.jsjsyzdh.202404012
基于虚拟关系知识图可自适应聚合的推荐算法
Recommendation Algorithm Based on Adaptive Aggregation of Virtual Relationship Knowledge Graph
李源 1杨谋均2
作者信息
- 1. 湖南省电子信息产业研究院,湖南长沙 410001
- 2. 中车株洲电力机车研究所有限公司,湖南株洲 412001
- 折叠
摘要
Abstract
In the era of information explosion,recommendation algorithms have become an effective means to cope with information overload.In recent years,graph neural networks(GNN)have been widely applied in recommendation algo-rithms due to their powerful modeling capabilities and advantages in addressing cold start issues.A joint training framework based on deep reinforcement learning and GNN-R is proposed in this paper to address the fixed-layer and aggregation strate-gy issues in GNN-R.By employing interval experience replay and delayed reward mechanisms,the learning process of the recommendation model is optimized.Building upon this,two optimization algorithms for adaptively optimizing the aggrega-tion layers and virtual relation quantities in GNN-R are proposed,enhancing the performance of the VRKG4Rec model.Ex-perimental results demonstrate significant performance improvements of the two optimization algorithms compared to the VRKG4Rec model.关键词
推荐算法/图神经网络/深度强化学习/知识图谱Key words
recommendation algorithm/graph neural network/deep reinforcement learning/knowledge graph分类
信息技术与安全科学引用本文复制引用
李源,杨谋均..基于虚拟关系知识图可自适应聚合的推荐算法[J].计算技术与自动化,2024,43(4):73-78,6.
