图神经网络推荐研究进展

图神经网络(graph neural network, GNN)具有从图的领域对数据进行特征提取和表示的优势，近年来成为人工智能研究的热点，图神经网络推荐也是推荐系统研究的一个新方向。本文对GNN模型进行深入研究的基础上，分析了GNN推荐过程，并从无向单元图推荐、无向二元图推荐、无向多元图推荐3个方面详细讨论了现有GNN推荐研究取得的主要进展及不足，阐明了现有GNN推荐研究中存在的主要难点，最后提出了GNN上下文推荐、GNN跨领域推荐、GNN群组推荐、GNN推荐的可解释性等未来GNN推荐的研究方向。

Research advances in graph neural network recommendation

Graph neural network (GNN) has the advantage of feature extraction and representation of data from the field of Graph. In recent years, it has become a hotspot of artificial intelligence research, and the recommendation of Graph Neural Network is also a new direction of recommendation system research. Based on the in-depth study of GNN model, this paper analyzes the GNN recommendation process, and discusses in detail the main progress and deficiencies of GNN recommendation studies from three aspects: undirected unit graph recommendation, undirected binary graph recommendation and undirected multivariate graph recommendation. The main difficulties in existing GNN recommendation studies are clarified, and the research directions of GNN recommendation in the future, including GNN contextual recommendation, GNN cross-domain recommendation, GNN group recommendation, and GNN recommendation’s interpretability, and so on, are pointed out in the end.