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The visual analytics community has proposed several user modeling algorithmsto capture and analyze users' interaction behavior in order to assist users indata exploration and insight generation. For example, some can detectexploration biases while others can predict data points that the user willinteract with before that interaction occurs. Researchers believe thiscollection of algorithms can help create more intelligent visual analyticstools. However, the community lacks a rigorous evaluation and comparison ofthese existing techniques. As a result, there is limited guidance on whichmethod to use and when. Our paper seeks to fill in this missing gap bycomparing and ranking eight user modeling algorithms based on their performanceon a diverse set of four user study datasets. We analyze exploration biasdetection, data interaction prediction, and algorithmic complexity, among othermeasures. Based on our findings, we highlight open challenges and newdirections for analyzing user interactions and visualization provenance.

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