
Areas of Expertise
Personalization and Recommender Systems; Business Intelligence; Knowledge Discovery and Data Mining; Human-Computer Interaction
Academic Degrees
- PhD, University of Minnesota, 2012
- MS, Temple University, 2007
Professional Experience
- Associate Professor, Kelley School of Business, Indiana University, 2019-present
- Assistant Professor, Kelley School of Business, Indiana University, 2012-2019
Awards, Honors & Certificates
- ISR Best Published Paper Award 2020, Information Systems Research (ISR), 2021
- ISS Sandra A. Slaughter Early Career Award, INFORMS Information Systems Society, 2020
- Best Student Paper Award, INFORMS Workshop on Data Science (DS), 2020
- Outstanding Associate Editor Award, International Conference on Information Systems (ICIS), 2019
- Poets & Quants’ Top 50 Undergraduate Professors, 2018
- Nominated for Sauvain Undergraduate Teaching Award, Kelley School of Business, Indiana University, 2018
- Trustee’s Teaching Award, Indiana University, 2017
- Nominated for Indiana University Outstanding Junior Faculty Award, Indiana University, 2017
- Nominated for Best Paper Award, Workshop on Information Technologies and Systems, 2016
- Nominated for Best Paper Award, Conference on Information Systems and Technology, 2016
- Nominated for Trustee’s Teaching Award, Indiana University, 2015
- 3M Nontenured Faculty Award, Kelley School of Business, Indiana University, 2014-2016
- ISS Nunamaker-Chen Dissertation Award, INFORMS Information Systems Society, 2013
- Best Paper Award, Workshop on Information Technologies and Systems (WITS'11), 2011
- Theodore C. and Peggy L. Willoughby Fellowship in Management Information Systems, MIS Quarterly, 2011
- McNamara Woman's Fellowship, University of Minnesota, 2011
Selected Publications
- Kim, A., Yang, M., and Zhang, J. (2022). When Algorithms Err: Differential Impact of Early vs. Late Errors on Users' Reliance on Algorithms. ACM Transactions on Computer-Human Interaction, in press.
- Adomavicius, G., Bockstedt, J., Curley, S., and Zhang, J. (2022). Effects of Personalized Recommendations Versus Aggregate Ratings on Post-Consumption Preference Responses. MIS Quarterly, 46(1), 627-644.
- Adomavicius, G., Bockstedt, J., Curley, S., and Zhang, J. (2022). Recommender Systems, Ground Truth, Bias, and Preference Pollution. AI Magazine, 43(2), 177-189.
- Adomavicius,, G., Bockstedt, J., Curley, S., and Zhang, J. (2021). Effects of Personalized and Aggregate Top-N Recommendation Lists on User Preference Ratings. ACM Transactions on Information Systems, 39(2) article no. 13.
- Zhang, J., Adomavicius, G., Gupta, A., and Ketter, W. (2020). Consumption and Performance: Understanding Longitudinal Dynamics of Recommender Systems Via an Agent-Based Simulation Framework. Information Systems Research, 31(1), 76–101.
- Cheng, X., Zhang, J. and Yan, L. (2020). Understanding the Impact of Individual Users’ Rating Characteristics on Predictive Accuracy of Recommender Systems. INFORMS Journal on Computing, 32(2), 303-320.
- Adomavicius, G., Bockstedt, J., Curley, S., Zhang, J., and Ransbotham, S. (2019). Hidden Side Effects of Recommendation Systems. MIT Sloan Management Review, 60(2), 13-15.
- Adomavicius, G., Bockstedt, J., Curley, S., and Zhang, J. (2019). Reducing Recommender Systems Biases: An Investigation of Rating Display Designs. MIS Quarterly, 43(4), 1321-1341.
- Zhang, J., and Curley, S. (2018). Exploring Explanation Effects on Consumers’ Trust in Online Recommender Agents. International Journal of Human-Computer Interactions, 34(5), 421-432.
- Adomavicius, G., Bockstedt, J., Curley, S., and Zhang, J. (2018). Effects of Online Recommendations on Consumers’ Willingness to Pay. Information Systems Research, 29(1), 84-102.
- Adomavicius, G., and Zhang, J. (2016). Classification, Ranking and Top-K Stability of Recommendation Algorithms. INFORMS Journal on Computing, 28(1), 129-147.
- Adomavicius, G., and Zhang, J. (2015). Improving Stability of Recommender Systems: A Meta-Algorithmic Approach. IEEE Transactions on Knowledge and Data Engineering, 27(6), 1573-1587.
- Adomavicius, G., Bockstedt, J., Curley, S. and Zhang, J. (2013). Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effect. Information Systems Research, 24(4), 956-975.
- Adomavicius, G., and Zhang, J. (2012). Impact of Data Characteristics on Recommender Systems Performance. ACM Transactions on Management Information Systems, 3(1), 3:1-3:17.
- Adomavicius, G., and Zhang, J. (2012). Stability of Recommendation Algorithms. ACM Transactions on Information Systems, 30(4), 23:1-23:31.
Edited on August 25, 2022