Graph convolution networks (GCN), which recently becomes new state-of-the-art method for graph node classification, recommendation and other applications, has not been successfully applied to industrial-scale search engine yet. In this proposal, we introduce our approach, namely SearchGCN, for embedding-based candidate retrieval in one of the largest e-commerce search engine in the world. Empirical studies demonstrate that SearchGCN learns better embedding representations than existing methods, especially for long tail queries and items. Thus, SearchGCN has been deployed into JD.com's search production since July 2020.

翻译：最近成为图表节点分类、建议和其他应用方面最新先进方法的图变网络(GCN)尚未成功应用于工业规模搜索引擎,在这项提案中,我们引入了我们的方法,即SearchGCN, 将基于候选人的检索嵌入世界上最大的电子商务搜索引擎之一。经验性研究表明,SearchGCN比现有方法更好地嵌入代表,特别是长尾查询和项目。因此,SearchGCN自2020年7月以来就被投入JD.com的搜索生产。