Machine learning for targeted display advertising: transfer learning in action

Abstract

This paper presents a detailed discussion of problem formulation and data representation issues in the design, deployment, and operation of a massive-scale machine learning system for targeted display advertising. Notably, the machine learning system itself is deployed and has been in continual use for years, for thousands of advertising campaigns (in contrast to simply having the models from the system be deployed). In this application, acquiring sufficient data for training from the ideal sampling distribution is prohibitively expensive. Instead, data are drawn from surrogate domains and learning tasks, and then transferred to the target task. We present the design of this multistage transfer learning system, highlighting the problem formulation aspects. We then present a detailed experimental evaluation, showing that the different transfer stages indeed each add value. We next present production results across a variety of advertising clients from a variety of industries, illustrating the performance of the system in use. We close the paper with a collection of lessons learned from the work over half a decade on this complex, deployed, and broadly used machine learning system.

A survey of transfer learning

“…Online display web advertising is a growing industry where transfer learning is used to optimally predict targeted ads. In the paper by Perlich [90], a transfer learning approach is employed that uses the weighted outputs of multiple source classifiers to enhance a target classifier trained to predict targeted online display advertising results. The paper by Kan [56] addresses the field of facial recognition and is able to use face image information from one ethnic group to improve the learning of a classifier for a different ethnic group.…”

Understanding programmatic TV advertising

“…8). The computational approach also has much in common with direct marketing and customer relationship management (CRM), in that it makes decisions at the customer (or household or device) level informed by customer databases and increasingly machine learning (Perlich et al 2014), and directly observes outcomes such as conversion. Advertisers can still deliver to mass audiences, but they are now doing so by making individual choices on who to address advertising to, when to deliver it, and what commercial message will lead to the best outcomes, all in near real time.…”

Bigger is Better, but at What Cost?Estimating the Economic Value of Incremental Data Assets

“…19 The company uses both first-party and thirdparty user behavior data to match the right ads to the right users. As is common in the ad tech industry, Dstillery has its own native data but also has access to data segments sold by third parties.…”