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

TL;DR

This paper provides a comprehensive comparison of eight user modeling algorithms in visual analytics, evaluating their effectiveness in predicting user interactions and detecting exploration biases across multiple datasets.

Contribution

It offers a rigorous evaluation and ranking of existing user modeling techniques, addressing a key gap in guidance for selecting appropriate methods.

Findings

Identification of the most effective algorithms for interaction prediction.

Insights into the strengths and weaknesses of different models.

Highlighting open challenges and future research directions.

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.