Jingjing Zhang is an associate professor of Operations and Decision Technologies at the Kelley School of Business, Indiana University. She received her Ph.D. in business administration with an Information Systems concentration from the University of Minnesota. Her research interests include personalization techniques, recommender systems, and human-computer interactions. Her research has appeared in journals including MIS Quarterly, Information Systems Research, INFORMS Journal on Computing, IEEE Transactions on Knowledge and Data Engineering, and ACM Transactions on Information Systems.
Industry Expertise (1)
Areas of Expertise (7)
Personalization and Recommender Systems
Data Mining and Knowledge Discovery
Nominated for Sauvain Undergraduate Teaching Award, Kelley School of Business, Indiana University
Trustee’s Teaching Award, Indiana University
Nominated for Best Paper Award, Workshop on Information Technologies and Systems
Nominated for Indiana University Outstanding Junior Faculty Award, Indiana University
Nominated for Best Paper Award, Conference on Information Systems and Technology
3M Nontenured Faculty Award, Kelley School of Business, Indiana University
ISS Nunamaker-Chen Dissertation Award, INFORMS Information Systems Society
Nominated for Trustee’s Teaching Award, Indiana University
Theodore C. and Peggy L. Willoughby Fellowship in Management Information Systems, MIS Quarterly
Best Paper Award, Workshop on Information Technologies and Systems (WITS'11)
McNamara Woman's Fellowship, University of Minnesota
University of Minnesota: Ph.D. 2012
Temple University: M.S. 2007
2020 We develop a general agent-based modeling and computational simulation approach to study the impact of various factors on the temporal dynamics of recommender systems’ performance. The proposed agent-based simulation approach allows for comprehensive analysis of longitudinal recommender systems performance under a variety of diverse conditions, which typically is not feasible with live real-world systems.
2019 In this study, we investigate how individual users’ rating characteristics affect the user-level performance of recommendation algorithms. We measure users’ rating characteristics from three perspectives: rating value, rating structure, and neighborhood network embeddedness. We study how these three categories of measures influence the predictive accuracy of popular recommendation algorithms for each user.
2019 Prior research has shown that online recommendations have a significant influence on consumers’ preference ratings and economic behavior. Specifically, biases induced by observing personalized system recommendations can lead to distortions in users’ self-reported preference ratings after consumption of an item, thus contaminating the users’ subsequent inputs to the recommender system.
2018 This paper focuses on the measure of recommendation stability, which reflects the consistency of recommender system predictions. Stability is a desired property of recommendation algorithms and has important implications on users' trust and acceptance of recommendations. Prior research has reported that some popular recommendation algorithms can suffer from a high degree of instability. I
2016 Recommendation stability measures the extent to which a recommendation algorithm provides predictions that are consistent with each other. Several approaches have been proposed in prior work to defining, measuring, and improving the stability of recommendation algorithms.