FREE ENBIS Webinar by Ran Jin: "PRIME: A Personalized Recommendation for Information Visualization Methods via Extended Matrix Completion"

15 October 2020; 16:00 – 16:45

PRIME: A Personalized Recommendation for Information Visualization Methods via Extended Matrix Completion

Adapting user interface designs for specific tasks performed by different users is a challenging yet important problem. Automatically adapting visualization designs to users and contexts (e.g., tasks, display devices, environments, etc.) can theoretically improve human-computer interaction to acquire insights from complex datasets. However, effectiveness of any specific visualization is moderated by individual differences in knowledge, skills, and abilities for different contexts. A modeling framework called Personalized Recommender System for Information visualization Methods via Extended matrix completion (PRIME) is proposed for recommending the optimal visualization designs for individual users in different contexts. PRIME quantitatively models covariates (e.g., psychological and behavioral measurements) to predict recommendation scores (e.g., perceived complexity, mental workload, etc.) for users to adapt the visualization specific to the context. An evaluation study was conducted and showed that PRIME can achieve satisfactory recommendation accuracy for adapting visualization, even when there are limited historical data. PRIME can make accurate recommendations even for new users or new tasks based on historical wearable sensor signals and recommendation scores. This capability contributes to designing a new generation of visualization systems that will adapt to users’ states.


Dr. Ran Jin is an Associate Professor and the Director of Laboratory of Data Science and Visualization at the Grado Department of Industrial and Systems Engineering at Virginia Tech. He received his Ph.D. degree in Industrial Engineering from Georgia Tech, Atlanta, his Master’s degrees in Industrial Engineering, and in Statistics, both from the University of Michigan, Ann Arbor, and his bachelor’s degree in Electronic Engineering from Tsinghua University, Beijing. His research focuses on data fusion in smart manufacturing, computation services, and cognitive-based interactive visualization. He is currently serving as an Associate Editor for IISE Transactions, Associate Editor for Journal of Manufacturing Science and Engineering, and Associate Editor for INFORMS Journal on Data Science. He has been working with many leading manufacturing companies in aerospace, semiconductor, personal care, optical fiber industries. For more information about Dr. Jin, please visit his faculty website at Virginia Tech: