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HH 4100
1309 E. 10th Street
Bloomington, IN
47405
Jingjing Zhang
Associate Professor
Judith Norman Davis and Kim G. Davis Professor of Business Analytics
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 Editor for Information Systems Research (ISR), 2021-present
Guest Associate Editor for Decision Support Systems (DSS), special issue for WITS’2022, 2023-present
Program chair, Workshop on Information Technologies and Systems (WITS), December 2022
Conference chair, INFORMS Workshop on Data Science (DS), October 2022
Panel chair, Americas Conference on Information Systems (AMCIS), August 2022
Program chair, INFORMS Workshop on Data Science (DS), November 2021
Associate Editor for International Conference on Information Systems, 2017, 2019, 2021
Program chair, Workshop on Information Technologies and Systems (WITS), December 2020
Associate Editor for Journal of Business Analytics (JBA), 2018-present
Guest Associate Editor for MIS Quarterly (MISQ), special issue on “Managing AI,” 2020
Treasurer of INFORMS College on Artificial Intelligence (CAI), 2018-2020
Editorial review board for ISR special issue on “Humans, Algorithms, and Augmented Intelligence: The Future of Work, Organization and Society,” 2018
Guest Associate Editor for Journal of the Association for Information Systems (JAIS), special issue on “Addressing Societal Challenges through Analytics,” 2017-2018
Poster/Prototype chair, Workshop on Information Technologies and Systems (WITS), 2016
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. (2023). When Algorithms Err: Differential Impact of Early vs. Late Errors on Users' Reliance on Algorithms. ACM Transactions on Computer-Human Interaction, 30(1), article no. 14.
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.
Abstract
This article investigates the impact of rating data characteristics on the performance of several popular recommendation algorithms, including user-based and item-based collaborative filtering, as well as matrix factorization. We focus on three groups of data characteristics: rating space, rating frequency distribution, and rating value distribution. A sampling procedure was employed to obtain different rating data subsamples with varying characteristics; recommendation algorithms were used to estimate the predictive accuracy for each sample; and linear regression-based models were used to uncover the relationships between data characteristics and recommendation accuracy. Experimental results on multiple rating datasets show the consistent and significant effects of several data characteristics on recommendation accuracy.
Adomavicius, G., and Zhang, J. (2012). Stability of Recommendation Algorithms. ACM Transactions on Information Systems, 30(4), 23:1-23:31.
Abstract
The paper explores stability as a new measure of recommender systems performance. Stability is defined to measure the extent to which a recommendation algorithm provides predictions that are consistent with each other. Specifically, for a stable algorithm, adding some of the algorithm’s own predictions to the algorithm’s training data (for example, if these predictions were confirmed as accurate by users) would not invalidate or change the other predictions. While stability is an interesting theoretical property that can provide additional understanding about recommendation algorithms, we believe stability to be a desired practical property for recommender systems designers as well, because unstable recommendations can potentially decrease users’ trust in recommender systems and, as a result, reduce users’ acceptance of recommendations. In this paper, we also provide an extensive empirical evaluation of stability for six popular recommendation algorithms on four real-world datasets. Our results suggest that stability performance of individual recommendation algorithms is consistent across a variety of datasets and settings. In particular, we find that model-based recommendation algorithms consistently demonstrate higher stability than neighborhood-based collaborative filtering techniques. In addition, we perform a comprehensive empirical analysis of many important factors (e.g., the sparsity of original rating data, normalization of input data, the number of new incoming ratings, the distribution of incoming ratings, the distribution of evaluation data, etc.) and report the impact they have on recommendation stability.
