A Perspective on Targeted Advertising:Principles, Implementation, Controversies
Yahoo! vice-president - computational advertising sector
DEI - Conference Room
November 17th, 2011
Online user interaction is becoming increasingly personalized both via explicit means: customizations, options, add-ons, skins, apps, etc. and via implicit means, that is, deep data mining of user activities that allows automated personalized content and experiences, e.g. individualized top news stories, personalized ranking of search results, personal “radio stations” that capture idiosyncratic tastes from past choices, individually recommended purchases, and so on. On the other hand, the vast majority of providers of content and services (e.g. portals, search engines, social sites) are supported by advertising, which at core, is just a different type of information. Thus, not surprisingly, on-line advertising is becoming increasingly personalized as well, supported by the emerging new discipline of Computational Advertising.
The central problem of Computational Advertising is to find the "best match" between a given user in a given context and a suitable advertisement. The context could be a user entering a query in a search engine ("sponsored search"), a user reading a web page ("content match" and "display ads"), a user communicating via instant-messaging or via e-mail, a user interacting with a portable device, and many more. The information about the user can vary from scarily detailed to practically nil. The number of potential advertisements might be in the billions. Thus, depending on the definition of "best match" this problem leads to a variety of massive optimization and search problems, with complicated constraints. The solution to these problems provides the scientific and technical foundations of the online advertising industry, which according to E-Marketer, has achieved $26B dollars in revenue in 2010 in US alone, for the first time exceeding newspaper advertising revenue at “only” 22.8B dollars.
The focus of this talk is targeted advertising, a form of personalized advertising whereby advertisers specify the features of their desired audience, either explicitly, by specifying characteristics such as demographics, location, and context, or implicitly by providing examples of their ideal audience. A particular form of targeted advertising is behavioral targeting, where the desired audience is characterized by its past behavior. We will discuss how targeted advertising fits the optimization framework above, present some of the mechanisms by which targeted and behavioral advertising are implemented, and briefly survey the controversies surrounding behavioral advertising as a potential infringement on user privacy. We will conclude with some speculations about the future of personalized advertising and interesting areas of research.
Note: This talk represents the personal opinions of the author and do not necessarily reflect the views of Yahoo! Inc.
Dr. Andrei Broder (Yahoo! Research)
Andrei Broder is a Yahoo! Fellow and Vice President for Computational Advertising. He also serves as chief scientist of Yahoo!’s Advertising Product Group. Previously he was an IBM Distinguished Engineer and the CTO of the Institute for Search and Text Analysis in IBM Research. From 1999 until 2002 he was Vice President for Research and Chief Scientist at the AltaVista Company. He was graduated Summa cum Laude from Technion, the Israeli Institute of Technology, and obtained his M.Sc. and Ph.D. in Computer Science at Stanford University. His current research interests are centered on computational advertising, web technologies, context-driven information supply, and randomized algorithms. Broder has authored more than a hundred papers and was awarded thirty-two patents. He is a member of the US National Academy of Engineering, a fellow of ACM and of IEEE, and past chair of the IEEE Technical Committee on Mathematical Foundations of Computing.