CS&E Profile: John Riedl

Collaborative filtering, collaborative systems, and information filtering.

Ph.D. 1990, M.S. 1985, Computer Sciences, Purdue University

B.S. 1983, Mathematics, Notre Dame University

About

Professor Riedl specializes in collaborative filtering, systems, and information filtering. He also often speaks as an expert on the topic of online social networks.

In 2006, he was named a Senior Member of the IEEE and also won the Best Paper Award at the Computer Supported Cooperative Work (CSCW) Conference. Riedl has also received the Commerce Technology Award, The MIT Sloan School Award for Innovation in E-Commerce, and at least half a dozen teaching awards.

Riedl has served on many program committees and has authored more than 50 publications, including one book, journal and conference papers, short articles and book chapters. He is a member of the ACM and IEEE organizations, and an Editorial Board member for the Journal of Electronic Commerce Technologies.

Research

My research focus is on collaborative systems that support human interaction through computer systems. My career goal is to understand how to develop and apply computer technology to the problems of human organizations.

One of the biggest such problems is getting the right information to the right people. The Internet has democratized the publishing process. Now, anyone who wants can publish anything they want, just by creating a Web site. We humans are hopelessly overmatched by the increasing volumes of information that are published. Collaborative filtering is a technology that enables us to all work together to sift through the millions of documents on any topic to find those that are most appropriate for each of us. Collaborative filtering works by learning which kinds of documents each of us likes, and finding other people who share our interests.

We are working on improving collaborative filtering by extending the amount and type of information it presents to users, the range of interfaces that it supports, and the other types of filtering algorithms with which it can be combined. For instance, we have explored ways to create explanations of collaborative filtering recommendations so users can understand why documents were recommended to them. We are also exploring community interfaces to collaborative filtering, which have the potential to strengthen the relationships between people in a group by helping them discover what they have in common with others in the group. We have also looked at communities in which some members of the community are not people, but are information filtering agents helping the people work more effectively.

Across our entire research program, our goal is to understand how computers can be used to help people process information more efficiently, and work together better.