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. The outcomes of this project will be (1) a publication at the ACM CHI conference and (2) integration in our platform for adaptive UIs.

Output

Model of visual search and pointing in menus. The code will be available on GitHub

The integration of the model in our platform for adaptive UI. The code will be available on GitHub

A demo of the system will be available online

A publication at the conference ACM CHI

Project Partners:

  • Sorbonne Université, CNRS, ISIR, Gilles BAILLY
  • Sorbonne Université, Gilles Bailly
  • Aalto University, Kashyap Todi

Primary Contact: Gilles BAILLY, Sorbonne Université, CNRS, ISIR