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Graph convolution networks (GCN), which recently becomes new state-of-the-artmethod for graph node classification, recommendation and other applications,has not been successfully applied to industrial-scale search engine yet. Inthis proposal, we introduce our approach, namely SearchGCN, for embedding-basedcandidate retrieval in one of the largest e-commerce search engine in theworld. Empirical studies demonstrate that SearchGCN learns better embeddingrepresentations than existing methods, especially for long tail queries anditems. Thus, SearchGCN has been deployed into JD.com's search production sinceJuly 2020.

SearchGCN: Powering Embedding Retrieval by Graph Convolution Networks for E-Commerce Search