# Predicting Human Performance in Vertical Menu Selection Using Deep Learning

arXiv: https://arxiv.org/abs/1803.05073 · HTML: https://mlsanity.com/paper/1803.05073/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2018.

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

- [Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques](https://mlsanity.com/paper/2204.07641/) (2022)
- [MenuCraft: Interactive Menu System Design with Large Language Models](https://mlsanity.com/paper/2303.04496/) (2023)
- [Large-Scale Modeling of Mobile User Click Behaviors Using Deep Learning](https://mlsanity.com/paper/2108.05342/) (2021)
- [Predicting Human Performance in Vertical Hierarchical Menu Selection in Immersive AR Using Hand-gesture and Head-gaze](https://mlsanity.com/paper/2206.09480/) (2022)

## Abstract (arXiv)

Predicting human performance in interaction tasks allows designers or developers to understand the expected performance of a target interface without actually testing it with real users. In this work, we present a deep neural net to model and predict human performance in performing a sequence of UI tasks. In particular, we focus on a dominant class of tasks, i.e., target selection from a vertical list or menu. We experimented with our deep neural net using a public dataset collected from a desktop laboratory environment and a dataset collected from hundreds of touchscreen smartphone users via crowdsourcing. Our model significantly outperformed previous methods on these datasets. Importantly, our method, as a deep model, can easily incorporate additional UI attributes such as visual appearance and content semantics without changing model architectures. By understanding about how a deep learning model learns from human behaviors, our approach can be seen as a vehicle to discover new patterns about human behaviors to advance analytical modeling.