A Sublinear, Massive-scale Look-alike Audience Extension System
published: Oct. 12, 2016, recorded: August 2016, views: 1101
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Look-alike audience extension is a practically effective way to customize high-performance audience in on-line advertising. With look-alike audience extension system, any advertiser can easily generate a set of customized audience by just providing a list of existing customers without knowing the detailed targetable attributes in a sophisticated advertising system. In this paper, we present our newly developed graph-based look-alike system in Yahoo! advertising platform which provides look-alike audiences for thousands of campaigns. Extensive experiments have been conducted to compare our look-alike model with three other existing look-alike systems using billions of users and millions of user features. The experiment results show that our developed graph-based method with nearest-neighbor filtering outperforms other methods in comparison by more than 50% regarding conversion rate in app-install ad campaigns.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !