图结构学习(Graph Structure Learning, GSL)通过优化图结构,增强图的表示能力和性能。GSL能够更好地捕捉图数据中节点之间的关系,从而促进信息的有效传播。图结构优化在商品链接预测中的应用研究旨在通过改进商品间关系的图结构,提高...图结构学习(Graph Structure Learning, GSL)通过优化图结构,增强图的表示能力和性能。GSL能够更好地捕捉图数据中节点之间的关系,从而促进信息的有效传播。图结构优化在商品链接预测中的应用研究旨在通过改进商品间关系的图结构,提高预测精度与推荐效果。在电商平台中,商品间的复杂关系往往通过图结构表示,其中节点代表商品,边代表商品间的关联或共同特征。通过优化图的构建和学习方法,能够更准确地捕捉商品之间的潜在联系,从而提升链接预测的准确性和推荐质量。优化后的图结构可以帮助算法更好地处理大规模商品数据,增强模型的泛化能力,进而提升电商平台的个性化推荐系统,增加用户购买的可能性,并促进销售增长。本文提出了一种新的稀疏正则化与图结构学习模型搜索方法(SGSL)。通过引入边缘修剪的正则化项等技术,SGSL能够在节点不变分类任务中显著提高性能,同时减少在节点变化任务中搜索到错误边的风险。实验表明,SGSL能有效增强图神经网络模型的性能。Graph Structure Learning (GSL) enhances the representational capacity and performance of graphs by optimizing their structure. GSL better captures the relationships between nodes in graph data, which facilitates more effective information propagation. The application of graph structure optimization in product link prediction aims to improve prediction accuracy and recommendation performance by refining the graph structure that represents the relationships between products. In e-commerce platforms, the complex relationships between products are often represented through graph structures, where nodes represent products and edges represent associations or shared features. By optimizing graph construction and learning methods, the underlying relationships between products can be more accurately captured, thereby improving link prediction accuracy and recommendation quality. The optimized graph structure helps algorithms better handle large-scale product data, enhancing the model’s generalization ability, which in turn improves personalized recommendation systems, increases the likelihood of user purchases, and drives sales growth. This paper introduces a novel Sparse Regularization and Graph Structure Learning Model Search method (SGSL). By incorporating techniques such as edge pruning regularization, SGSL significantly improves performance in node-invariant classification tasks while reducing the risk of selecting incorrect edges in node-variant tasks. Experimental results show that SGSL effectively enhances the performance of graph neural network models.展开更多
文摘图结构学习(Graph Structure Learning, GSL)通过优化图结构,增强图的表示能力和性能。GSL能够更好地捕捉图数据中节点之间的关系,从而促进信息的有效传播。图结构优化在商品链接预测中的应用研究旨在通过改进商品间关系的图结构,提高预测精度与推荐效果。在电商平台中,商品间的复杂关系往往通过图结构表示,其中节点代表商品,边代表商品间的关联或共同特征。通过优化图的构建和学习方法,能够更准确地捕捉商品之间的潜在联系,从而提升链接预测的准确性和推荐质量。优化后的图结构可以帮助算法更好地处理大规模商品数据,增强模型的泛化能力,进而提升电商平台的个性化推荐系统,增加用户购买的可能性,并促进销售增长。本文提出了一种新的稀疏正则化与图结构学习模型搜索方法(SGSL)。通过引入边缘修剪的正则化项等技术,SGSL能够在节点不变分类任务中显著提高性能,同时减少在节点变化任务中搜索到错误边的风险。实验表明,SGSL能有效增强图神经网络模型的性能。Graph Structure Learning (GSL) enhances the representational capacity and performance of graphs by optimizing their structure. GSL better captures the relationships between nodes in graph data, which facilitates more effective information propagation. The application of graph structure optimization in product link prediction aims to improve prediction accuracy and recommendation performance by refining the graph structure that represents the relationships between products. In e-commerce platforms, the complex relationships between products are often represented through graph structures, where nodes represent products and edges represent associations or shared features. By optimizing graph construction and learning methods, the underlying relationships between products can be more accurately captured, thereby improving link prediction accuracy and recommendation quality. The optimized graph structure helps algorithms better handle large-scale product data, enhancing the model’s generalization ability, which in turn improves personalized recommendation systems, increases the likelihood of user purchases, and drives sales growth. This paper introduces a novel Sparse Regularization and Graph Structure Learning Model Search method (SGSL). By incorporating techniques such as edge pruning regularization, SGSL significantly improves performance in node-invariant classification tasks while reducing the risk of selecting incorrect edges in node-variant tasks. Experimental results show that SGSL effectively enhances the performance of graph neural network models.
