MSR NYC Data Science Seminar Series #4 – What Makes us Human? Machine Learning Challenges in Digital Advertising

Digital advertising is one of the largest and open playgrounds for machine learning, data mining and related analytic approaches. This talk will touch on a number of challenges which arise in this environment: 1) high volume data streams of around 30 Billion daily consumer touch points, 2) low latency requirements on scoring and automated bidding decisioning within 100ms and 3) adversarial modeling in the light of advertising fraud and bots. Specifically, we will discuss an automated learning system implemented at Dstillery, that uses privacy friendly data representation to build sparse targeting models for thousands of products in Millions of dimensions. The solution incorporates ideas from transfer learning, Bayesian priors, stochastic gradient descent, hashing and learning rate estimation. On the sidelines, but of no less importance, are topics on bid optimization, data reliability, cross-device identification and observational methods for causal inference. Finally, I will touch on a few higher-level lessons around incentive misalignments/measurement issues in the advertising industry and pose the paradox of big data and predictive modeling: You never have the data you need.