Heterogeneous Graph Neural Networks for Large-Scale Bid Keyword Matching

by   Zongtao Liu, et al.

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.


page 1

page 2

page 3

page 4


AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query

Paper recommendation with user-generated keyword is to suggest papers th...

Keyword Optimization in Sponsored Search Advertising: A Multi-Level Computational Framework

In sponsored search advertising, keywords serve as an essential bridge l...

Keyword Targeting Optimization in Sponsored Search Advertising: Combining Selection and Matching

In sponsored search advertising (SSA), advertisers need to select keywor...

Heterogeneous Graph Neural Network for Recommendation

The prosperous development of e-commerce has spawned diverse recommendat...

Graph-based keyword search in heterogeneous data sources

Data journalism is the field of investigative journalism which focuses o...

Graph Matching Networks for Learning the Similarity of Graph Structured Objects

This paper addresses the challenging problem of retrieval and matching o...

Structural Textile Pattern Recognition and Processing Based on Hypergraphs

The humanities, like many other areas of society, are currently undergoi...

Please sign up or login with your details

Forgot password? Click here to reset