摘要:图卷积网络是一种针对图信号的深度学习模型，由于具有强大的特征表征能力得到了广泛应用.推荐系统可视为图信号的链接预测问题，因此近年来提出了使用图卷积网络解决推荐问题的方法.推荐系统中存在用户与商品间的异质顶点交互和用户（或商品）内部的同质顶点交互，然而，现有方法中的图卷积操作要么仅在异质顶点间进行，要么仅在同质顶点间进行，留下了提升此类推荐系统性能的空间.考虑到这一问题，提出了一种新的基于图卷积网络的推荐算法，使用两组图卷积操作同时利用两种不同的交互信息，其中异质顶点卷积用于挖掘交互图谱域中存在的连接信息，同质顶点卷积用于使相似顶点具有相近表示.实验结果表明，该算法比现有算法具有更优的精度.



Abstract:Graph convolutional network (GCN) is a deep learning model for graph signal processing and has been used in many real-world applications due to its powerful ability of feature extraction. As the recommendation problem can be viewed as link prediction of graph signals, recently several GCN based methods have been proposed for recommender systems. A recommender system involves two kinds of interactions, with one representing interactions between users and items and the other representing interactions among users (or items). However, existing methods focus on either heterogeneous or homogeneous interactions only, thus their modeling expressiveness is limited. In this study, a new GCN based recommendation algorithm is proposed to jointly utilize these two types of interactions. Specifically, a heterogeneous convolutional operator is used to mine information from the spectrum of user-item graphs, while a homogeneous convolutional operator is used to enforce similar vertices to be similar in the hidden space. Finally, the experiments on benchmark datasets show that the proposed method achieves better performance compared with several state-of-the-art methods.



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