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
• Engineering
• Mathematics
• Advertising
• Social psychology
• Business
• Ecology
• Management science

Selected publications

• Balancing Domestic and Global Perspectives: Evaluating Dual-Calibration and LLM-Generated Nudges for Diverse News Recommendation

  Open MIND · 2026-03-06

  preprintSenior author

  In this study, we applied the ``personalized diversity nudge framework'' with the goal of expanding user reading coverage in terms of news locality (i.e., domestic and world news). We designed a novel topic-locality dual calibration algorithmic nudge and a large language model-based news personalization presentation nudge, then launched a 5-week real-user study with 120 U.S. news readers on the news recommendation experiment platform POPROX. With user interaction logs and survey responses, we found that algorithmic nudges can successfully increase exposure and consumption diversity, while the impact of LLM-based presentation nudges varied. User-level topic interest is a strong predictor of user clicks, while highlighting the relevance of news articles to prior read articles outperforms generic topic-based and no personalization. We also demonstrate that longitudinal exposure to calibrated news may shift readers' reading habits to value a balanced news digest from both domestic and world articles. Our results provide direction for future work on nudging for diverse consumption in news recommendation systems.

  DOI

• Balancing Domestic and Global Perspectives: Evaluating Dual-Calibration and LLM-Generated Nudges for Diverse News Recommendation

  ArXiv.org · 2026-03-06

  articleOpen accessSenior author

  In this study, we applied the ``personalized diversity nudge framework'' with the goal of expanding user reading coverage in terms of news locality (i.e., domestic and world news). We designed a novel topic-locality dual calibration algorithmic nudge and a large language model-based news personalization presentation nudge, then launched a 5-week real-user study with 120 U.S. news readers on the news recommendation experiment platform POPROX. With user interaction logs and survey responses, we found that algorithmic nudges can successfully increase exposure and consumption diversity, while the impact of LLM-based presentation nudges varied. User-level topic interest is a strong predictor of user clicks, while highlighting the relevance of news articles to prior read articles outperforms generic topic-based and no personalization. We also demonstrate that longitudinal exposure to calibrated news may shift readers' reading habits to value a balanced news digest from both domestic and world articles. Our results provide direction for future work on nudging for diverse consumption in news recommendation systems.

  PublisherOA PDF

• Socially Embedded Agents in Organizational Contexts: Bulk Email as a Design Example

  Progress in IS · 2025-01-01

  book-chapterSenior author
  PublisherDOI

• Multi-Prompting Scenario-based Movie Recommendation with Large Language Models: Real User Case Study

  2025-04-23 · 1 citations

  articleSenior author
  PublisherDOI

• Development, usability, and randomized pilot feasibility study of a mobile application prototype supporting self-management of accidental bowel leakage

  Continence · 2025-08-06 · 1 citations

  articleOpen access

  Aims: Patients need support in managing conservative interventions for fecal incontinence (FI). To address this need, a three-phased study aimed to develop and test the usability of a mobile application prototype (App-p) supporting patient self-management of FI and examine the feasibility of conducting a randomized controlled pilot study of App-p use. Methods: Phase 1: The App-p was developed. Phase 2: App-p usability was tested. Phase 3: In a pilot feasibility study, patients with FI from an American continence/urogynecology practice were randomly assigned 1:1 to a usual care control group (UCC-Group) or usual care and App-p use group (APP-Group) and followed for five weeks. Study activities for both groups were completion of electronic data forms at baseline and five weeks and, at five weeks, a call with their clinician and an interview. Descriptive quantitative and qualitative analyses were conducted. Results: The I'M ABLe App-p described 11 conservative interventions for FI, each with a journal for self-reporting intervention performance. App-p usability was very good (System Usability Score = 72 (12.5) (mean (SD)). Twenty eligible participants were randomized as planned. All study activities were completed by 60 % (6/10) of the UCC-Group and 80 % (8/10) of the APP-Group. All journals were completed by 80 % (8/10) of the APP-Group versus 30 % (3/10) of the UCC-Group. The APP-Group accessed the App-p 27 (5-45) days (median (range)), describing it as easy-to-use, convenient, and increasing self-management accountability. One UCC-Group participant and two APP-Group participants withdrew. Conclusion: Results show feasibility of conducting a randomized controlled study using the App-p and support its further development.

  PublisherDOI

• Patients’ experiences with self-management of conservative interventions for fecal incontinence

  Continence · 2025-07-17

  articleOpen access

  Aims: To describe patients' experience self-managing conservative interventions for fecal incontinence (FI) during usual FI care. Methods: Community-living adults with FI recruited from urogynecology and continence clinics participated in a pilot study developing a mobile application for FI self-management support. Data were collected using a demographics questionnaire, FI severity index tool, and semi-structured interviews about participants' experience self-managing conservative interventions for FI which were part of their usual care treatment plan. Interviews were recorded and transcribed verbatim using online video software. Transcripts were analyzed using content analysis. Results: Data from 17 women, aged 30 to ≥60 years, 9 White, 8 Black/African American, who had FI ranging from less than one year to more than 10 years were analyzed in this study. Themes of responses described starting interventions soon after receiving them, mixed opinions about ease of performing some interventions (e.g., pelvic floor muscle exercises, completing diaries), barriers to performing interventions (e.g., forgetfulness, no time), practical strategies facilitating self-management (e.g., setting alarms, keeping a schedule), emotional strategies that were supportive and motivating (e.g., maintaining hope, seeing positive results), and advice about self-management to others with FI (seek help, give it time). Conclusion: Patient experiences provided clinicians with specific topics to target for patient education and ways to support themselves in self-managing FI.

  PublisherDOI

• Co-Authoring the Self: A Human-AI Interface for Interest Reflection in Recommenders

  ArXiv.org · 2025-10-10

  preprintOpen accessSenior author

  Natural language-based user profiles in recommender systems have been explored for their interpretability and potential to help users scrutinize and refine their interests, thereby improving recommendation quality. Building on this foundation, we introduce a human-AI collaborative profile for a movie recommender system that presents editable personalized interest summaries of a user's movie history. Unlike static profiles, this design invites users to directly inspect, modify, and reflect on the system's inferences. In an eight-week online field deployment with 1775 active movie recommender users, we find persistent gaps between user-perceived and system-inferred interests, show how the profile encourages engagement and reflection, and identify design directions for leveraging imperfect AI-powered user profiles to stimulate more user intervention and build more transparent and trustworthy recommender experiences.

  PublisherOA PDFDOI

• Why They Come And Go: A Case Study of Productive Flyby Users and Their Rating Integrity Challenge in Movie Recommenders

  2025-03-24

  articleOpen accessSenior author

  We present a case study of productive flyby users (PFB users) on a recommendation website.These users exhibit counterintuitive behavior: they input a large amount of data during their first visit but never return.This phenomenon can have both positive and negative impacts on the system.On the positive side, their high productivity contributes a substantial amount of data.On the negative side, they may input inappropriate ratings that violate the assumptions of recommendation algorithms, potentially undermining system performance.To better understand the nature and causes of this behavior, we investigated their motivations, expectations, reasons for leaving, and the potential risks associated with their ratings using a mixed-methods approach.Specifically, we conducted interviews with 11 users, surveyed 41 users, and analyzed the impact of 1,000 PFB users on the performance of recommendation algorithms for regular users.Our findings revealed diverse motivations among PFB users.Some engaged with the system merely to pass the time, while others had unrealistic expectations of the recommender system.Regarding rating quality, 27% of surveyed users admitted to rating movies they had not seen, citing reasons such as browsing too quickly or attempting to manipulate the algorithm.Notably, users who reported leaving because they were "just killing time and forgot about the website" were the most likely to rate unseen movies.Overall, PFB users significantly influence recommendation algorithms and their performance for regular users.While some subgroups negatively affect prediction accuracy, others provide

  PublisherOA PDFDOI

• Beyond the Individual: A Community-Engaged Framework for Ethical Online Community Research

  Proceedings of the ACM on Human-Computer Interaction · 2025-10-16 · 2 citations

  article

  Online community research routinely poses minimal risk to individuals, but does the same hold true for online communities? In response to high-profile breaches of online community trust and increased debate in the social computing research community on the ethics of online community research, this paper investigates community-level harms and benefits of research. Through 9 participatory-inspired workshops with four critical online communities (Wikipedia, InTheRooms, CaringBridge, and r/AskHistorians), we found researchers should engage more directly with communities' primary purpose by rationalizing their methods and contributions in the context of community goals to equalize the beneficiaries of community research. To facilitate deeper alignment of these expectations, we present the FACTORS (Functions for Action with Communities: Teaching, Overseeing, Reciprocating, and Sustaining) framework for ethical online community research. Finally, we reflect on our findings by providing implications for researchers and online communities to identify and implement functions for navigating community-level harms and benefits.

  PublisherDOI

• The MovieLens Beliefs Dataset: Collecting Pre-Choice Data for Online Recommender Systems

  2024-10-08 · 3 citations

  articleOpen accessSenior author

  An increasingly important aspect of designing recommender systems involves considering how recommendations will influence consumer choices. This paper addresses this issue by introducing a method for collecting user beliefs about un-experienced goods – a critical predictor of choice behavior. We implemented this method on the MovieLens platform, resulting in a rich dataset that combines user ratings, beliefs, and observed recommendations. We document challenges to such data collection, including selection bias in response and limited coverage of the product space. This unique resource empowers researchers to delve deeper into user behavior and analyze user choices absent recommendations, measure the effectiveness of recommendations, and prototype algorithms that leverage user belief data, ultimately leading to more impactful recommender systems. The dataset can be found at https://grouplens.org/datasets/movielens/ml_belief_2024/.

  PublisherOA PDFDOI

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

• ITR: Collaborative Research: Designing On-Line Communities to Enhance Participation -- Bridging Theory and Practice

  NSF · $1.3M · 2003–2010

• HCC: Small: Net Fishing: Pulling Valuable Tweets, Feeds, and Blogs from the Online Message Stream

  NSF · $500k · 2010–2015

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)