.png){kind=icon}
返回
Meta-learning on dynamic node clustering knowledge graph for cold-start recommendation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2024.128192.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Meta-learning has been introduced in the recommendation domain, and a possible direction to extend graph- based meta-learning is how to exploit higher-order information between nodes. The current studies primarily rely on pre-embedding methods to utilize user attributes, inadvertently overlooking the crucial co-occurrence patterns among users' features. This oversight hinders the discovery of implicit connections between users. Furthermore, in scenarios of infrequent interactions, the reliance on initial edge information constrains connections between nodes in the same category. This constraint compromises the update capability of node embeddings, leading to reduced recommendation accuracy. In this paper, a meta-learning recommendation method with a dynamic node clustering knowledge graph (DCKG) is proposed. By introducing user attribute nodes and a triple-gated attention networks, this approach facilitates connections between users based on attribute relationships and enables the balancing of information across various pathways. Additionally, the integration of a dynamic aggregation module frees nodes of the same category from initial connection constraints, fostering more direct connections and further augmenting the propagation capability of higher- order information. The experimental results show that DCKG achieves the excellent results in the cold-start recommendation. DCKG can extend the use of higher-order features in the graph by direct connection.
Keyword:
Cold-start recommendation
Knowledge graph
Dynamic node clustering
Meta-learning
Recommendation systems
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
The Relative Influence of Lesion Length and Other Stenosis Morphologies on Procedural Success of Coronary Intervention病变长度和其他狭窄形态对冠状动脉介入手术成功的相对影响.png)
Angiology
AngiologyIF0
PREAI
分享收藏分享收藏
查看更多内容