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Jingjing Zhang - Indiana University, Kelley School of Business. Bloomington, IN, US

Jingjing Zhang Jingjing Zhang

Associate Professor | Indiana University, Kelley School of Business

Bloomington, IN, UNITED STATES

Professor Zhang focuses on personalization and recommender systems, business intelligence, and knowledge discovery and data mining






IU CEWiT Faculty Alliance Salon, 02/21/14, Part 2: JingJing Zhang



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)

Human-Computer Interaction

Knowledge Discovery

Recommender Systems

Personalization and Recommender Systems

Personalization Systems

Business Intelligence

Data Mining and Knowledge Discovery

Accomplishments (11)

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


Education (2)

University of Minnesota: Ph.D. 2012

Temple University: M.S. 2007

Articles (5)

Consumption and Performance: Understanding Longitudinal Dynamics of Recommender Systems via an Agent-Based Simulation Framework

Information Systems Research

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.

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Understanding the Impact of Individual Users’ Rating Characteristics on the Predictive Accuracy of Recommender Systems

Informs Journal on Computing

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.

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Reducing Recommender System Biases: An Investigation of Rating Display Designs

MIS Quarterly

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.

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Improving Stability of Recommender Systems: A Meta-Algorithmic Approach

IEEE Transactions on Knowledge and Data Engineering

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

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Classification, Ranking, and Top-K Stability of Recommendation Algorithms

Informs Journal on Computing

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.

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