Budget pacing for targeted online advertisements at LinkedIn

Deepak Agarwal, Souvik Ghosh, Kai Wei, Siyu You

Abstract: Targeted online advertising is a prime source of revenue for many Internet companies. It is a common industry practice to use a generalized second price auction mechanism to rank advertisements at every opportunity of an impression. This greedy algorithm is suboptimal for both advertisers and publishers when advertisers have a finite budget. In a greedy mechanism high performing advertisers tend to drop out of the auction marketplace fast and that adversely affects both the advertiser experience and the publisher revenue. We describe a method for improving such ad serving systems by including a budget pacing component that serves ads by being aware of global supply patterns. Such a system is beneficial for both advertisers and publishers. We demonstrate the benefits of this component using experiments we conducted on advertising at LinkedIn.