基于多任务学习的图神经网络推荐模型研究

A graph neural network recommendation model based on multi-task learning

图神经网络处理非欧氏空间数据的强大能力促使越来越多的研究将其应用于推荐领域。然而,现有的基于图神经网络的推荐模型大多数仍然采用多个邻接矩阵来表示多种节点或边属性等异质信息,没有充分利用异质信息之间的交互。因此,提出一种新型的图神经网络推荐模型,把所有信息实体之间的丰富交互建模成异质图,并在异质图上使用稠密子图采样策略进行子图采样;此外,模型还加入多任务学习方法用于共同优化链接预测与推荐任务,使得模型学习到更好的节点表示,以提升推荐效果。2个公开数据集上的实验结果表明,所提模型相比基线模型,在Top-N推荐任务性能上有所提高。

The powerful ability of graph neural network to process non-Euclidean spatial data has prompted more and more people to pay attention to its application in the recommendation field.However,most of the existing recommendation models based on graph neural networks still use several adjacency matrices to represent heterogeneous information such as all kind of nodes or edge attributes,and fail to make full use of the interaction of heterogeneous information.Therefore,this paper proposes a new graph neural network recommendation model,which models the rich interactions between all information entities as heterogeneous graph and uses the dense subgraph sampling strategy for sampling the subgraphs of heterogeneous graph.In addition,the multi-task learning method is added to the model to jointly optimize the link prediction and recommendation tasks,so that the model learns a better node representation and effectively improves the recommendation results.Experiments on two public datasets show that,compared with the baseline models,the proposed model improves the performance of the Top-N recommendation task.