基于超图卷积神经网络的多行为感知服务推荐方法

Multi-behavior aware service recommendation based onhypergraph graph convolution neural network

针对现有服务推荐方法中高阶服务特征提取不够充分的问题,提出基于超图卷积神经网络的多行为感知服务推荐方法(MBSRHGNN).该方法根据服务交互类型和服务组合信息构建多重超图,基于谱分解理论和多重超图的功能结构特性以设计双通道超图卷积网络.利用切比雪夫多项式近似超图卷积核来降低计算复杂度;在超图卷积过程中,结合多行为推荐方法和自注意力机制度量多行为交互之间的重要性差异,提出HG-DiffPool超图池化方法来降低特征维度;通过融合服务嵌入向量和超图信号,学习不同服务的推荐概率分布;爬取真实服务数据,构造不同稀疏度的数据集进行实验.实验结果表明,所提的MBSRHGNN服务推荐方法能够适应数据高度稀疏的推荐场景,并且在推荐精确度和相关性上的表现优于现有基线方法.

A multi-behavior aware service recommendation method based on hypergraph graph convolutional neural network(MBSRHGNN)was proposed to resolve the problem of insufficient high-order service feature extraction in existing service recommendation methods.A multi-hypergraph was constructed according to user-service interaction types and service mashups.A dual-channel hypergraph convolutional network was designed based on the spectral decomposition theory with functional and structural properties of multi-hypergraph.Chebyshev polynomial was used to approximate hypergraph convolution kernel to reduce computational complexity.Self-attention mechanism and multi-behavior recommendation methods were combined to measure the importance difference between multibehavior interactions during the hypergraph convolution process.A hypergraph pooling method named HG-DiffPool was proposed to reduce the feature dimensionality.The probability distribution for recommending different services was learned by integrating service embedding vector and hypergraph signals.Real service data was obtained by the crawler and used to construct datasets with different sparsity for experiments.Experimental results showed that the MBSRHGNN method could adapt to recommendation scenario with highly sparse data,and was superior to the existing baseline methods in accuracy and relevance.