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Beyond Keyword Targeting: An End-to-End Ad Retrieval Framework for Sponsored Search

As the main revenue source for search engines, sponsored search system retrieves and allocates ads to display on the search result pages. Keyword targeting is widely adopted by most sponsored search systems as the basic model for expressing the advertiser's business and retrieving related ads. In this targeting model, the advertiser should cautiously select lots of keywords relevant to their business to optimize their campaigns, and the sponsored search system retrieves ads based on the relevance between queries and keywords. However, since there is a huge inventory of possible queries and the new queries grow dramatically, it is a great challenge for advertisers to identify and collect lots of relevant bid keywords for their ads, and it also takes great effort to select and maintain high-quality keywords and set corresponding match types for them. In the meantime, the keyword targeting leads to a multi-stage retrieval architecture as it contains the matching between query and keywords and the matching between keywords and ads. The retrieval funnel based on keyword targeting cannot achieve straightforward and optimal matching between search queries and ads. Consequently, traditional keyword targeting method gradually becomes the bottleneck of optimizing advertisers' campaigns and improving the monetization of search ads. In this paper, we present an end-to-end ad retrieval framework for sponsored search. This framework can break the limits of keyword targeting and can achieve direct matching from query to related ads. The framework has been deployed at Baidu's sponsored search system and the experimental result validates its effectiveness. We will describe the design and architecture of this framework, and hope that this framework can give some inspiration for the sponsored search industry.