陈卓　李涵　杜军威

摘   要：基于图神经网络的推荐算法通过从图中获取知识，提高了推荐的可解释性. 然而随着推荐系统网络数据规模的不断扩大，用户-项目评分矩阵呈现出稀疏性问题，图神经网络难以学习到高质量的网络节点特征，导致推荐质量下降. 本文将图神经网络与异质信息网络相结合，提出一种基于异质图神经网络的推荐算法. 该算法使用异質信息网络对多源异质数据进行联合解码，将注意力机制引入用户-项目交互网络和用户社交网络的用户、项目聚合表示过程，从而实现用户-项目交互和用户社交两类网络间的节点及拓扑结构特征的有效融合. 两个公开数据集上的对比实验结果表明，本文提出的算法在不断稀疏化的数据集上的推荐误差比基线方法少40%.

关键词：图神经网络;异质信息网络;推荐;网络表示学习

中图分类号：TP18                              文献标志码：A

Research on Recommendation Algorithm Based

on Heterogeneous Graph neural Network

CHEN Zhuo LI Han DU Junwei

（School of Information Science and Technology，Qingdao University of Science and Technology，Qingdao 266061，China）

Abstract：By acquiring knowledge from a graph，the recommendation algorithm based on the graph neural network improves the recommendation interpretability. However，with the continuous expansion of the network data scale of the recommended system，the user-item scoring matrix displays a sparsity problem，which makes the graph neural network difficult to learn high quality network node features，and finally leads to the decline of recommendation quality. In this paper，a recommendation algorithm based on heterogeneous graph neural network is proposed by combining graph neural network with heterogeneous information network. This algorithm uses heterogeneous information network to decode multi-source heterogeneous data. And the attention mechanism is introduced into the user and item aggregation process of user-item interaction network and user social network，in order to realize the effective fusion of Node and topology characteristics of user-item interaction and user social networks. The experiment on two continuous sparse datasets show that the recommendation error of the algorithm proposed in this paper is 40% less than that of the baseline method.

Key words：graph neural network;heterogeneous information network;recommendations;network representation learning

随着电子商务和社交媒体平台的快速发展，推荐系统已经成为许多企业不可或缺的工具. 一个高效的推荐系统能通过准确地捕捉用户的偏好，发现用户可能感兴趣的项目，从而提高用户对平台的满意度，提高用户的保留率.

推荐系统根据用户的兴趣和项目属性来评估用户对项目的偏好. 由于用户兴趣和项目特征都用压缩向量表示，因此学习用户/项目交互和其他辅助信息，如社会关系和知识图的嵌入表示，成为该邻居需解决的关键问题.

在推荐系统中，大多……