[TMP-070] Learning Individual Users Strategies for Adaptive UIs

Contact person: Gilles Bailly (Gilles.Bailly@sorbonne-universite.fr) 

Internal Partners:

1. Sorbonne Université, Gilles Bailly
2. Aalto University, Kashyap Todi and Antti Oulasvirta  

External Partners:

1. University of Luxembourg, Luis Leiva  

 

Adapting user interfaces (UIs) requires taking into account both positive and negative effects that changes may have on the user. A carelessly picked adaptation may impose high costs — for example, due to surprise or relearning effort. It is essential to consider differences between users as the effect of an adaptation depends on the user’s strategies, e.g. how each user searches for information in a UI. This microproject extends an earlier collaboration between partners on model-based reinforcement learning for adaptive UIs by developing methods to account for individual differences. Here, we first develop computational models to explain and predict users’ visual search and pointing strategies when searching within a UI. We apply this model to infer user strategies based on interaction history, and adapt UIs accordingly.

Results Summary

This micro-project reinforces the collaborations between Sorbonne Université, Aalto University and University of Luxembourg with weekly meetings. It aims at elaborating computational models of visual search in adaptive User Interfaces. We defined different visual search strategies in adaptive menus as well as promising interactive mechanisms to revisit how to to design menus. The Elaboration of the model is in progress. Concretely, we achieved 4 things:

1. Created a model of visual search and pointing in menus. The code is available on GitHub
2. The integration of the model in our platform for adaptive UI. The code is available on GitHub
3. A demo of the system
4. A publication at the conference ACM CHI

Tangible Outcomes

1.  Adapting User Interfaces with Model-based Reinforcement Learning. Kashyap Todi, Gilles Bailly, Luis A. Leiva, Antti Oulasvirta.In CHI ’21: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems https://dl.acm.org/doi/fullHtml/10.1145/3411764.3445497
2. summary of project’s key findings https://www.kashyaptodi.com/adaptive/
3. paper presentation from CHI’21
4. Interaction preview
5. Video summarizing the project