Jingjing Zhang

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

• Assistant Professor, Kelley School of Business, Indiana University, 2012-present

Awards, Honors & Certificates

• 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

• Zhang, J. and S. Curley (2018), “Exploring Explanation Effects on Consumers’ Trust in Online Recommender Agents.," International Journal of Human-Computer Interactions (IJHCI), 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 (ISR), 29(1): 84-102.
• Adomavicius, G., and Zhang, J. (2016), “Classification, Ranking and Top-K Stability of Recommendation Algorithms”, INFORMS Journal on Computing (JOC), 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 (TKDE), 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 J. Zhang (2012), "Impact of Data Characteristics on Recommender Systems Performance," ACM Transactions on Management Information Systems (TMIS), 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, Gediminas, and Jingjing Zhang (2012), "Stability of Recommendation Algorithms," ACM Transactions on Information Systems (TOIS), 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.