Contrastive Learning‑based Simplified Graph Convolutional Network Recommendation

Contrastive Learning‑based Simplified Graph Convolutional Network Recommendation

[Purposes] Considering the problems of the existing Graph Convolutional Network (GCN) recommendation models, such as low model convergence efficiency, over-smoothing, and deteriorative recommendations for long-tail items caused by the effect of high-degree nodes on presentation learning, a Contrasti...

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Bibliographic Details
Main Authors: YU Yuchen, WU Siqi, ZHAO Qinghua, WU Xuhong, WANG Lei
Format: Article
Language:English
Published: Editorial Office of Journal of Taiyuan University of Technology 2025-05-01
Series:Taiyuan Ligong Daxue xuebao
Subjects:
graph convolutional network
self-supervised learning
contrastive learning
long-tail item
Online Access:https://tyutjournal.tyut.edu.cn/englishpaper/show-2423.html
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https://tyutjournal.tyut.edu.cn/englishpaper/show-2423.html

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