# Learning Efficient Representations of Mouse Movements to Predict User Attention

arXiv: https://arxiv.org/abs/2006.01644 · HTML: https://mlsanity.com/paper/2006.01644/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2020.

## Built on by (most cited first, 4 of 4)

- [Integrating Cognitive Processing Signals into Language Models: A Review of Advances, Applications and Future Directions](https://mlsanity.com/paper/2504.06843/) (2025)
- [A Versatile Dataset of Mouse and Eye Movements on Search Engine Results Pages](https://mlsanity.com/paper/2507.08003/) (2025)
- [Orchestrating Attention: Bringing Harmony to the 'Chaos' of Neurodivergent Learning States](https://mlsanity.com/paper/2602.07865/) (2026)
- [Query Abandonment Prediction with Recurrent Neural Models of Mouse Cursor Movements](https://mlsanity.com/paper/2101.09066/) (2021)

## Abstract (arXiv)

Tracking mouse cursor movements can be used to predict user attention on heterogeneous page layouts like SERPs. So far, previous work has relied heavily on handcrafted features, which is a time-consuming approach that often requires domain expertise. We investigate different representations of mouse cursor movements, including time series, heatmaps, and trajectory-based images, to build and contrast both recurrent and convolutional neural networks that can predict user attention to direct displays, such as SERP advertisements. Our models are trained over raw mouse cursor data and achieve competitive performance. We conclude that neural network models should be adopted for downstream tasks involving mouse cursor movements, since they can provide an invaluable implicit feedback signal for re-ranking and evaluation.