自监督混合图神经网络的会话推荐模型

Self-supervised Hybrid Graph Neural Network for Session-Based Recommendation

基于会话的推荐旨在利用匿名会话预测用户行为。现有基于图神经网络(GNN)的会话推荐算法大多仅针对当前会话提取用户偏好,却忽略了来自其他会话的高阶多元关系从而影响推荐精度。此外,由于会话推荐所采用的短时交互序列包含的信息非常有限,使其更容易受到数据稀疏性的影响。针对上述问题,提出了自监督混合图神经网络会话推荐模型(SHGN)。该模型首先通过将原始数据构建为三个视图来描述会话与物品关系,然后通过多头图注意力网络捕获会话内部物品的低阶转换信息,提出了残差图卷积网络捕获物品和会话的高阶转换信息;最后融合自监督学习(SSL)作为辅助任务,通过最大化不同通道学习到的会话嵌入的互信息,对原始数据进行数据增强从而提升推荐性能。为了验证该方法的有效性,在Tmall、Diginetica、Nowplaying、Yoochoose四个基准数据集上与SR-GNN、GCE-GNN、DHCN等主流基线模型进行了对比实验,实验结果在P@20、MRR@20等性能指标上均取得了一定提升。

Session-based recommendation aims to predict user actions based on anonymous sessions.Most of the existing session recommendation algorithms based on graph neural network(GNN)only extract user preferences for the current session,but ignore the high-order multivariate relationships from other sessions,which affects the recommendation accuracy.Moreover,session-based recommendation suffers more from the problem of data sparsity due to the very limited short-term interactions.To solve the above problems,this paper proposes a model named selfsupervised hybrid graph neural network(SHGN)for session-based recommendation.Firstly,the model describes the relationship between sessions and objects by constructing the original data into three views.Next,a graph attention network is used to capture the low-order transitions information of items within a session,and then a residual graph convolutional network is proposed to mine the high-order transitions information of items and sessions.Finally,selfsupervised learning(SSL)is integrated as an auxiliary task.By maximizing the mutual information of session embeddings learnt from different views,data augmentation is performed to improve the recommendation performance.In order to verify the effectiveness of the proposed method,comparative experiments with mainstream baseline models such as SR-GNN,GCE-GNN and DHCN are carried out on four benchmark datasets of Tmall,Diginetica,Nowplaying and Yoochoose,and the results are improved in P@20,MRR@20 and other performance indices.