Computational Advertising – Andrei Broder (Yahoo!)
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 challenge 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 challenge leads to a variety of massive optimization and search problems, with complicated constraints. This talk will give an introduction to this area focusing on the IR and NLP connections.
Andrei Broder is a Fellow and Vice President for Computational Advertising in Yahoo! Research. He also serves as Chief Scientist of Yahoo’s Advertising Technology 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 graduated Summa cum Laude from the Technion, and obtained his M.Sc. and Ph.D. in Computer Science at Stanford University. His current research interests are centered on computational advertising, web search, context-driven information supply, and randomized algorithms. Broder is co-winner of the Best Paper award at WWW6 (for his work on duplicate elimination of web pages) and at WWW9 (for his work on mapping the web). He has authored more than ninety papers and was awarded twenty-eight patents. He is an ACM Fellow, an IEEE fellow, and past chair of the IEEE Technical Committee on Mathematical Foundations of Computing.