Joseph Konstan

Joseph A. Konstan has been involved in ACM's activities for over 25 years: participating in, developing and nurturing new technical areas, serving on key task forces and committees, and leading several of ACM's major Boards and working groups. He has demonstrated a volunteer spirit that has served as an example and inspiration for others who have had the opportunity to work with him. His long involvement in and deep insight into ACM's operation and governance has made him a trusted source of advice for ACM's elected leadership, volunteers, and staff.

Joe's service started in 1994 within ACM SIGCHI's conferences, eventually becoming SIGCHI's President (2003-2006) and chair of the SIG Governing Board (2006-2008) and as a member of ACM's Executive Committee. During that time he served on a task force on the future of ACM-W. 

As co-chair of the Publications Board (2013-2022), Joe served on ACM's Extended Executive Committee, providing insightful advice and recommendations to the elected leadership. In that regard, he served on ACM's Strategic Planning Workgroup (2013-2014), which set the priorities and roadmap for ACM's continued growth and development. He also served on the task force on the future of the Journal of the ACM, chaired the task force on ACM's future directions in Health and Medical Informatics, and ACM's Strategic Planning Working Group.

Joe Konstan is the epitome of a dedicated volunteer. His selfless goal is to make the organization the best it can be. He is inclusive in his efforts to bring people together to make the best decisions for ACM and the communities it serves. His many contributions to ACM have been, and no doubt will continue to be, outstanding.

Recommender systems have become ubiquitous. Whether shopping at Amazon.com, selecting videos at Netflix, or getting news and information, we have become accustomed to sophisticated personalization software that uses our preference data, together with the preference data of others, to provide personalized recommendations for content or products. Automated collaborative filtering, which launched the field of recommender systems in 1994, was introduced, refined, and commercialized by the team of John Riedl, Paul Resnick, Joseph A. Konstan, Neophytos Iacovou, Peter Bergstrom, Mitesh Suchak, Brad Miller, David Maltz, Jon Herlocker, Lee Gordon, Sean McNee, and Shyong (Tony) K. Lam through the GroupLens family of systems.

The first generation GroupLens system (1994) was used to create personalized recommendations of Usenet news articles. The second generation (1995-1996) also targeted Usenet news articles, and led to the commercial launch of Net Perceptions, spawning what is now a vibrant recommender systems industry. After commercialization, the team launched MovieLens in 1997. MovieLens, a web-based research platform still operating today, has been used by more than 100,000 users; its online experiments and published datasets have led to hundreds of published advances in the field of recommender systems.

Prior to GroupLens, most personalization systems were based entirely on building profiles of content preferences, and then applying them to new content. While this technique worked when interests were topical (e.g., choosing news about a favorite football club), they did not handle less feature-based preferences or distinguish among topical items by quality or taste. In 1992, Xerox PARC's Tapestry project introduced the term and concept "collaborative filtering", creating a centralized database where annotations could be stored and retrieved, but application of this idea was limited by the need to manually form queries and explore the database. The GroupLens team introduced automation to the process, which proved to be the fundamental breakthrough that enabled wide-ranging research and commercial applications.

Through the founding of Net Perceptions, GroupLens had an enormous impact on e-commerce and information portals. Early customers, including Amazon.com, CDnow, and Art.com, demonstrated that the software generalized to a wide variety of domains, and validated the usefulness of collaborative filtering recommendation through explicit and implicit user ratings . Net Perceptions grew to become a $1 billion company, providing recommender systems to leading retail and information companies around the world. Recommender systems grew into an extensive research field, drawing from machine learning, human computer interaction, ecommerce, information retrieval, databases, and other areas of computer science. ACM's annual Recommender Systems Conference brings together leading researchers and practitioners. The field was recently energized by the $1 million Netflix Challenge.