Recommender Systems: Beyond Machine Learning
ABSTRACT: Recommender systems help users find items of interest and help websites and marketers select items to promote. Today's recommender systems incorporate sophisticated technology to model user preferences, model item properties, and leverage the experiences of a large community of users in the service of better recommendations. Yet all too often better recommendations--at least by traditional measures of accuracy and precision--fail to meet the goal of improving user experience. This talk will take a look at successes and failures in moving beyond basic machine learning approaches to recommender systems to emphasize factors tied to user behavior and experience. Along the way, we will explore a generalizable approach to combining human-centered evaluation with data mining and machine learning techniques.
BIO: Joseph Konstan is Distinguished McKnight University Professor and Distinguished University Teaching Professor in the Department of Computer Science and Engineering at the University of Minnesota. His research addresses a variety of human-computer interaction issues, including personalization (particularly through recommender systems), eliciting on-line participation, and designing computer systems to improve public health. He is probably best known for his work in collaborative filtering recommenders (the GroupLens project, work which won the ACM Software Systems Award and Seoul Test of Time Award). Dr. Konstan received his Ph.D. from the University of California, Berkeley in 1993. He is a Fellow of the ACM, IEEE, and AAAS, and a member of the CHI Academy. Konstan is co-Chair of the ACM Publications Board, served as President of ACM SIGCHI and as a member of the ACM Council.