  Digital advertising is a critical part of many e-commerce platforms such as
Taobao and Amazon. While in recent years a lot of attention has been drawn to
the consumer side including canonical problems like ctr/cvr prediction, the
advertiser side, which directly serves advertisers by providing them with
marketing tools, is now playing a more and more important role. When speaking
of sponsored search, bid keyword recommendation is the fundamental service.
This paper addresses the problem of keyword matching, the primary step of
keyword recommendation. Existing methods for keyword matching merely consider
modeling relevance based on a single type of relation among ads and keywords,
such as query clicks or text similarity, which neglects rich heterogeneous
interactions hidden behind them. To fill this gap, the keyword matching problem
faces several challenges including: 1) how to learn enriched and robust
embeddings from complex interactions among various types of objects; 2) how to
conduct high-quality matching for new ads that usually lack sufficient data.
  To address these challenges, we develop a
heterogeneous-graph-neural-network-based model for keyword matching named
HetMatch, which has been deployed both online and offline at the core sponsored
search platform of Alibaba Group. To extract enriched and robust embeddings
among rich relations, we design a hierarchical structure to fuse and enhance
the relevant neighborhood patterns both on the micro and the macro level.
Moreover, by proposing a multi-view framework, the model is able to involve
more positive samples for cold-start ads. Experimental results on a large-scale
industrial dataset as well as online AB tests exhibit the effectiveness of
HetMatch.
