Designing Systems That Adapt to Their Users

Full-day tutorial, presented on July 29th, 2002, by:
Anthony Jameson,
Joseph Konstan, and
John Riedl,
at AAAI 2002,
the 18th National Conference on Artificial Intelligence,
Edmonton, Alberta, Canada,
July 28 - August 1, 2002.

We thank the tutorial participants for their lively and creative contributions, which helped to demonstrate the added value of the full-day tutorial format.


Links to Tutorial Materials

The following PDF file contains the complete tutorial notes:

The following file contains just the tables of contents for all of the sections of the tutorial:


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Description in the AAAI 2002 Advance Program

Content and Format

Personalized recommendation of products, documents, and collaborators has become an important way of meeting user needs in commerce, information provision, and community services, whether on the web, through mobile interfaces, or through traditional desktop interfaces. This tutorial first reviews the types of personalized recommendation that are being used commercially and in research systems. It then systematically presents and compares the underlying AI techniques, including recent variants and extensions of collaborative filtering, demographic and case-based approaches, and decision-theoretic methods. The properties of the various techniques will be compared within a general framework, so that participants learn how to match recommendation techniques to applications and how to combine complementary techniques.

The full-day format makes it possible to include a session in which participants actively work together on a concrete problem, as well as in-depth discussion of application contexts, case studies, and key social issues.

The tutorial presupposes a general knowledge of AI. Some previous familiarity with issues of personalized recommendation is desirable but not essential.

Information About the Presenters

Anthony Jameson is a principal researcher at DFKI (the German Research Center for Artificial Intelligence) and adjunct professor of computer science at the International University in Germany. He has published widely on personalized recommendation and user adaptation since the early 1980s. He has presented related tutorials in the CHI, IJCAI, IUI, and UM conference series.

Joe Konstan and John Riedl are Associate Professors in the Department of Computer Science and Engineering at the University of Minnesota. John Riedl cofounded the pioneering GroupLens recommender system project with Paul Resnick in 1992, and he and Joe Konstan have been codirecting the project since 1995. Riedl and Konstan also cofounded Net Perceptions, a company that has commercialized the results of their research since 1996. They have published broadly in the area of recommender systems, and they have presented related tutorials at the ACM conferences on E-Commerce and on Computer-Supported Cooperative Work.