Machine Learning Based Interaction Technique Selection for 3D User Interfaces

Abstract

A 3D user interface can be adapted in multiple ways according to each user's needs, skills and preferences. Such adaptation can consist in changing the user interface layout or its interaction techniques. Personalization systems which are based on user models can automatically determine the configuration of a 3D user interface in order to fit a particular user. In this paper, we propose to explore the use of machine learning in order to propose a 3D selection interaction technique adapted to a target user. To do so, we built a dataset with 51 users on a simple selection application in which we recorded each user profile, his/her results to a 2D Fitts Law based pre-test and his/her preferences and performances on this application for three different interaction techniques. Our machine learning algorithm based on Support Vector Machines (SVMs) trained on this dataset proposes the most adapted interaction technique according to the user profile or his/her result to the 2D selection pre-test. Our results suggest the interest of our approach for personalizing a 3D user interface according to the target user but it would require a larger dataset in order to increase the confidence about the proposed adaptations.

3DPlasticToolkit: Plasticity for 3D User Interfaces

“…Exploring new solutions for learning user preferences: For now, the user's preferences scores are declared for each user. It would be interesting to analyze the current user profile and the current task parameters, to automatically determine the user preferences scores, as suggested in [12] to build general, group and individual user models, by using machine learning algorithms.…”