Recommender Systems: Looking Back and Forward

Recommender systems were inspired by information overload challenges presented by then-new widespread use of the Internet (and the invention of the web), and early innovators addressed challenges of insufficient information and intractable computational approaches. The result was a wealth of techniques, experiments, and systems. But today’s environment is different. Our tools and models are more powerful. And the question arises—what is special about “recommender systems” that is not simply the application of today’s best computing tools to personalization problems? In this talk I present one vision of where the recommender systems community might continue to innovate and add value in the long term.

Joseph A. Konstan is Distinguished McKnight University Professor and Distinguished University Teaching Professor of Computer Science and Engineering at the University of Minnesota where he also serves as Assoc. Dean for Research in the College of Science and Engineering. His work focuses on human-centered computing with particular focus on recommender systems and computing applications in behavioral health. He holds an A.B. from Harvard and an M.S. and Ph.D. from the University of California, Berkeley, all in Computer Science. He is a Fellow of the ACM, IEEE, and AAAS, a member of the CHI Academy, and winner of the ACM Software System Award and SIGCHI Lifetime Achievement in Reseearch Award. Prof. Konstan is also known for two open online course sequences on the Coursera platform—one on Recommender Systems and the other on User Interface Design and Evaluation.