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In this talk, we will discuss how Baidu is applying the state-of-the-art data-driven deep learning technology to address the keyword matching problem, which is of great importance in sponsored search. Keyword matching deals with linking users' queries and advertisers' keywords under the restriction of different match types (exact match, phrase match, and smart match). Three challenges exist in this problem: the semantic gap between queries and keywords, the matching type restriction, and the scalability problem induced by large volumes of queries and keywords.

Our talk will consist of 3 parts: a) how to make use of the data-driven DNN models to mitigate the semantic gap, b) how to use BERT to judge the matching type of a query-keyword pair, c) how to use knowledge distilling, synonymous keywords compression, and online-offline mixed structure to deploy the BERT model in a real industrial environment.

These data-driven deep learning approaches have been successfully applied in Baidu's sponsored search, which yield a significant increase in commercial revenue without degrading users' experience. We hope our method would shed light on the further design of the industrial sponsored search system.

Presenter: Yijiang Liang

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