A Unified Comparison of User Modeling Techniques for Predicting Data Interaction and Detecting Exploration Bias

Sunwoo Ha, Shayan Monadjemi, Roman Garnett, Alvitta Ottley

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

The visual analytics community has proposed several user modeling algorithms to capture and analyze users' interaction behavior in order to assist users in data exploration and insight generation. For example, some can detect exploration biases while others can predict data points that the user will interact with before that interaction occurs. Researchers believe this collection of algorithms can help create more intelligent visual analytics tools. However, the community lacks a rigorous evaluation and comparison of these existing techniques. As a result, there is limited guidance on which method to use and when. Our paper seeks to fill in this missing gap by comparing and ranking eight user modeling algorithms based on their performance on a diverse set of four user study datasets. We analyze exploration bias detection, data interaction prediction, and algorithmic complexity, among other measures. Based on our findings, we highlight open challenges and new directions for analyzing user interactions and visualization provenance.

Original languageEnglish
Pages (from-to)483-492
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Volume29
Issue number1
DOIs
StatePublished - Jan 1 2023
Externally publishedYes

Funding

This work is supported in part by the National Science Foundation under Grant No. OAC-2118201 and IIS-2142977.

Keywords

  • Analytic Provenance
  • Benchmark Study
  • Machine Learning
  • User Interaction Modeling
  • Visual Analytics

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