Andrei Broder and Vanja Josifovski, of Yahoo! Research, have wrapped up their Stanford course again this fall. As always the lecture slides are a great intro to the area.
MS&E 239: Introduction to Computational Advertising
Computational advertising is an emerging new scientific sub-discipline, at the intersection of large scale search and text analysis, information retrieval, statistical modeling, machine learning, classification, optimization, and microeconomics. 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 watching a movie on a portable device, and so on. 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, and challenging data representation and access problems. The solution to these problems provides the scientific and technical foundations for the $20 billion online advertising industry.
This course aims to provide a good introduction to the main algorithmic issues and solutions in computational advertising, as currently applied to building platforms for various online advertising formats. At the same time we intend to briefly survey the economics and marketplace aspects of the industry, as well as some of the research frontiers. The intended audience are students interested in the practical and theoretical aspects of web advertising.
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