Machine Learning for Display Advertising
Foster Provost (New York University)
Abstract
Most on-line advertisements are display ads, yet as compared to sponsored search, display advertising has received relatively little attention in the research literature. Nonetheless, display advertising is a hotbed of application for machine learning technologies. In this talk, I will discuss some of the relevant differences between online display advertising and traditional advertising, such as the ability to profile and target individuals and the associated privacy concerns, as well as differences from search advertising, such as the relative irrelevance of clicks on ads and the concerns over the content next to which brands’ ads appear. Then I will dig down and discuss how these issues can be addressed with machine learning. I will focus on two main results based on work with the successful machine-learning based firm Media6degrees. (i) Privacy-friendly “social targeting” can be quite effective, based on identifying browsers that share fine-grained interests with a brand’s existing customers–as exhibited through their browsing behavior. (ii) Clicks often are a poor surrogate for conversions for training targeting models, but there are effective alternatives.
This work was done in collaboration with Brian Dalessandro, Rod Hook, Alan Murray, Claudia Perlich, and Xiaohan Zhang.
Foster Provost is Professor, NEC Faculty Fellow, and Paduano Fellow of Business Ethics (Emeritus) at the NYU Stern School of Business. He just retired as Editor-in-Chief of the journal Machine Learning, and in 2001 he co-chaired the program of the ACM KDD conference. He is Chief Scientist for Coriolis Ventures, a NYC-based early stage venture and incubation firm focusing to a large extent on advertising technology. One of his main research interests is predictive modeling with social network data, most recently for on-line advertising. This work won the 2009 INFORMS Design Science Award. His other main research interest these days is the focused intervention of human resources for machine learning, especially based on micro-outsourcing (e.g., Mechanical Turk). Foster has applied these ideas in practice to applications including on-line advertising, targeted marketing, network diagnosis, fraud detection, counterterrorism, and others.
Visualization and Modeling of the Joint Behavior of Two Long Tailed Random Variables
Art Owen (Stanford University)
Abstract
Many of the variables relevant to online advertising have heavy tails. Keywords range from very frequent to obscure. Advertisers span a great size range. Host web sites range from very popular to rarely visited.
Much is known about the statistical properties of heavy tailed random variables. The Zipf distribution and Zipf-Mandelbrot distribution are frequently good approximations.
Much less attention has been paid to the joint distribution of two or more such quantities. In this work, we present a graphical display that shows the joint behavior of two long tailed random variables. For ratings data (Netflix movies, Yahoo songs) we often see a strong head to tail affinity where the major players of one type are over-represented with the minor players of the other. We look at several examples which reveal properties of the mechanism underlying the data. Then we present some mathematical models based on bipartite preferential attachment mechanisms and a Zipf-Poisson ensemble.
This is joint work with Justin Dyer.
Art Owen is Professor of Statistics, Stanford University. He has spent sabbaticals at the University of Chicago, ATT Bell Labs, MSRI and Google Inc. His research interests include statistical inference for high dimensional problems in Internet applications and bioinformatics. Owen is the inventor of empirical likelihood, now widely used in econometrics, and scrambled net quadrature, now widely used in computational finance and in computer graphics. He is an elected fellow of the Institute of Mathematical Statistics.