图自编码器推荐研究综述

图自编码器GAE是一种源自图神经网络的学习框架,在编码器中引入聚合邻域节点的思想,解码器对图结构数据进行解码,重构图结构数据;在模型中引入监督模块,可以提高图结构数据在模型中的嵌入完整性和数据生成的准确性;编解码可以采用不同的神经网络,从而利用不同神经网络的优点。近年来GAE推荐逐渐成为推荐系统研究的热点。从无监督学习与半监督学习方面分析了已有GAE推荐研究取得的进展;探讨了已有GAE推荐模型存在用户冷启动问题、可解释性差、模型复杂度高和难以处理数据的多源异构性等方面的问题;并从跨领域推荐,结合传统推荐方法,引入注意力机制,融合各类场景等研究方向对未来GAE推荐进行展望。

A review of graph auto-encoder recommendation

Graph Auto-Encoder (GAE) is a learning framework derived from graph neural network. It introduces the idea of clustering neighborhood nodes into the encoder, and the decoder decodes the graph structure data and reconstructs the graph structure data. The introduction of supervisory module in the model can improve the integrity of graph structure data embedded in the model and the accuracy of data generation.Encoder and decoder can use different neural networks to take advantage of the advantages different neural networks. In recent years, GAE recommendation has become a hot topic in recommendation system research. The progress of GAE recommendation research is analyzed from the aspects of unsupervised learning and semi-supervised learning. The deficiencies of the existing GAE recommendations, such as cold startup for users, uninterpretability, high model complexity, and multi-source heterogeneity of data, are discussed. In addition, it looks into the future of GAE recommendation from the perspectives of cross-field recommendation, combined with traditional recommendation methods, introduction of attention mechanism, and integration of various scenarios.