计算机科学
卷积神经网络
矩阵分解
图形
非负矩阵分解
人工智能
因式分解
机器学习
理论计算机科学
算法
量子力学
物理
特征向量
出处
期刊:PeerJ
[PeerJ]
日期:2024-02-29
卷期号:10: e1858-e1858
标识
DOI:10.7717/peerj-cs.1858
摘要
Managing user bias in large-scale user review data is a significant challenge in optimizing children’s book recommendation systems. To tackle this issue, this study introduces a novel hybrid model that combines graph convolutional networks (GCN) based on bipartite graphs and neural matrix factorization (NMF). This model aims to enhance the precision and efficiency of children’s book recommendations by accurately capturing user biases. In this model, the complex interactions between users and books are modeled as a bipartite graph, with the users’ book ratings serving as the weights of the edges. Through GCN and NMF, we can delve into the structure of the graph and the behavioral patterns of users, more accurately identify and address user biases, and predict their future behaviors. Compared to traditional recommendation systems, our hybrid model excels in handling large-scale user review data. Experimental results confirm that our model has significantly improved in terms of recommendation accuracy and scalability, positively contributing to the advancement of children’s book recommendation systems.
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