Machine Learning in Online Advertising held in conjunction with the 24 th Annual Conference on

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. NIPS Workshop: Machine Learning in Online Advertising (MLOAD 2010)