# From Interaction to Intent: Inferring User Objectives from Provenance Logs

arXiv: https://arxiv.org/abs/2607.04501 · HTML: https://mlsanity.com/paper/2607.04501/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 63 references that are in the map.
- First posted on arXiv 5 Jul 2026.

## Builds on (most cited first, 4 of 4)

- [A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration](https://mlsanity.com/paper/1811.12199/) (2018)
- [Left, Right, and Gender: Exploring Interaction Traces to Mitigate Human Biases](https://mlsanity.com/paper/2108.03536/) (2021)
- [A Grammar-Based Approach for Applying Visualization Taxonomies to Interaction Logs](https://mlsanity.com/paper/2201.03740/) (2022)
- [Analytic Provenance Datasets: A Data Repository of Human Analysis Activity and Interaction Logs](https://mlsanity.com/paper/1801.05076/) (2018)

## Abstract (arXiv)

The ability to automatically infer analytic intent from user interaction histories could enable interactive AI systems to proactively assist users during exploratory data analysis. In this paper, we examine whether provenance logs -- detailed records capturing sequences and timing of user interactions -- can be used to classify user intentions in visual exploration tasks. To investigate this, we record how participants interact with multiple multidimensional data projections across a range of analytic tasks, capturing fine-grained mouse interaction data throughout each session. We find that distinct behavioral signatures emerge across different analytic objectives. For instance, users examining properties of specific clusters exhibit markedly different interaction patterns compared to those searching for outliers. More importantly, we show that embedding contextual information into interaction provenance enables classifiers to predict user objectives that generalize across datasets and projection methods. These findings demonstrate that low-level interaction data can serve as a practical bridge to high-level analytic intent, contributing to the development of intent-aware visualization systems.