Paper Info
Title
An introduction to online targeted advertising: principles, implementation, controversies
Abstract
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 selection of 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 an emerging new scientific sub-discipline, 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, is estimated to achieve $25.8B dollars in revenue in 2010 in US alone, for the first time exceeding print 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.
Year
DOI
Venue
2011
10.1145/1943403.1943420
IUI
Keywords
Field
DocType
personalized advertising,computational advertising,on-line advertising,behavioral advertising,targeted advertising,print advertising revenue,online advertising industry,user activity,online user interaction,behavioral targeting,search engine,web pages,online advertising,data mining
Search advertising,Advertising research,Contextual advertising,World Wide Web,Behavioral targeting,Native advertising,Computer science,Online advertising,Keyword advertising,Targeted advertising,Multimedia
Conference
Citations 
PageRank 
References 
1
0.36
0
Authors
1
Name
Order
Citations
PageRank
Andrei Broder17357920.20