Joseph Konstan

About

Joseph Konstan is a professor in the Department of Computer Science & Engineering at the University of Minnesota, where he has been a faculty member since 1993. He holds the titles of Distinguished McKnight University Professor, Distinguished University Teaching Professor, and serves as the Associate Dean for Research for the College of Science and Engineering. His educational background includes a Ph.D. in Computer Science from the University of California, Berkeley, and an A.B. in Computer Science from Harvard University. Konstan's research broadly focuses on human-computer interaction, with particular emphasis on recommender systems—personalization software—and how these algorithms can be improved to enhance user experience. He also works on social computing, addressing challenges related to how technology supports or hinders collaboration, as well as health applications of technology, especially web and mobile behavioral interventions aimed at health improvement. His work is associated with the Human-Centered Computing division and the GroupLens Lab. Throughout his career, he has received numerous awards, including the Outstanding Contribution to ACM Award in 2023, the SIGIR Test of Time Award in 2017, and recognition as an ACM Fellow and IEEE Fellow. Konstan has also led significant research projects funded by agencies such as the National Science Foundation and NIH, contributing to advancements in learning engineering, health technology, and community Q&A experiments.

Research topics

• Computer Science
• Artificial Intelligence
• Data science
• Machine Learning
• Political Science
• Sociology
• Psychology
• Law
• Engineering ethics
• Marketing

Selected publications

• Six Human-Centered Artificial Intelligence Grand Challenges

  International Journal of Human-Computer Interaction · 2023 · 420 citations

  Widespread adoption of artificial intelligence (AI) technologies is substantially affecting the human condition in ways that are not yet well understood. Negative unintended consequences abound including the perpetuation and exacerbation of societal inequalities and divisions via algorithmic decision making. We present six grand challenges for the scientific community to create AI technologies that are human-centered, that is, ethical, fair, and enhance the human condition. These grand challenge…

  DOI

• Challenges and Future Directions of Computational Advertising Measurement Systems

  Journal of Advertising · 2020 · 89 citations

  Computational advertising (CA) is a rapidly growing field, but there are numerous challenges related to measuring its effectiveness. Some of these are classic challenges where CA offers a new aspect to the challenge (e.g., multi-touch attribution, bias), and some are brand-new challenges created by CA (e.g., fake data and ad fraud, creeping out customers). In this article, we present a measurement system framework for CA to provide a common starting point for advertising researchers to begin add…

  DOI

• Interactive Content Diversity and User Exploration in Online Movie Recommenders: A Field Experiment

  International Journal of Human-Computer Interaction · 2023-10-05 · 11 citations

  articleOpen accessSenior author

  Recommender systems often struggle to strike a balance between matching users' tastes and providing unexpected recommendations. When recommendations are too narrow and fail to cover the full range of users' preferences, the system is perceived as useless. Conversely, when the system suggests too many items that users don't like, it is considered impersonal or ineffective. To better understand user sentiment about the breadth of recommendations given by a movie recommender, we conducted interview…

  PublisherOA PDFDOI

• The Economics of Recommender Systems: Evidence from a Field Experiment on MovieLens

  2023-07-07 · 7 citations

  articleSenior author

  We conduct a 6 month field experiment on a movie-recommendation platform to identify if and how recommendation systems affect consumption. We use within-consumer randomization at the good level and elicit beliefs about unconsumed goods to disentangle exposure from informational effects. We have three experimental groups: (a) control, (b) exposed, and (c) recommended + exposed goods where only goods in (c) are recommended and we elicit beliefs about goods in (b) and (c). Comparing across these tr…

  PublisherDOI

• The challenge of organizational bulk email systems: Model and empirical studies

  Edward Elgar Publishing eBooks · 2024-03-12 · 4 citations

  book-chapterSenior author

  Large organizations use bulk email to communicate with employees about events, policies, organizational updates, and other information they feel will be useful or interesting to the employees. Such an organizational system has many stakeholders including information producers (often organizational leaders), communications professionals, recipients (employees), and management. We find this system to be inefficient - sending messages broadly appears free to senders, but shifts costs to recipients…

  PublisherDOI

Recent grants

• HCC-Small: Understanding and Supporting Online Question-Answering Sites

  NSF · $484k · 2008–2013

• CCRI: Planning: RecommendNews: Community Research Infrastructure for Online Field Experiments

  NSF · $100k · 2020–2023

• HCC: Small: Experiments in Community Q&A

  NSF · $516k · 2013–2018

Frequent coauthors

• John Riedl

  United Monolithic Semiconductor (France)

  62 shared

• Ruoyan Kong

  34 shared

• F. Maxwell Harper

  Amazon (Germany)

  31 shared

• John V. Carlis

  University of Minnesota

  22 shared

• Brian P. Bailey

  University of Illinois Urbana-Champaign

  20 shared

• Loren Terveen

  University of Minnesota

  19 shared

• Sean M. McNee

  16 shared

• Ruixuan Sun

  16 shared

Labs

• GroupLens LabPI

Awards & honors

• Outstanding Contribution to ACM Award (2023)
• President's Award for Outstanding Service (2022)
• SIGIR Test of Time Award (2017)
• James Chen Annual Award (2013)
• IEEE Fellow (2013)

Similar researchers at University of Minnesota

• Dan Knightsh-97
• Yousef Saadh-81
• Chad L. Myersh-72