Machine Learning Based Interaction Technique Selection for 3D User Interfaces

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.

[1]  Lior Rokach,et al.  Introduction to Recommender Systems Handbook , 2011, Recommender Systems Handbook.

[2]  Luca Chittaro,et al.  Dynamic generation of personalized VRML content: a general approach and its application to 3D e-commerce , 2002, Web3D '02.

[3]  Andrew S. Forsberg,et al.  Image plane interaction techniques in 3D immersive environments , 1997, SI3D.

[4]  I. Scott MacKenzie,et al.  Towards a standard for pointing device evaluation, perspectives on 27 years of Fitts' law research in HCI , 2004, Int. J. Hum. Comput. Stud..

[5]  Gerhard Fischer,et al.  User Modeling in Human–Computer Interaction , 2001, User Modeling and User-Adapted Interaction.

[6]  Alfred Kobsa,et al.  Generic User Modeling Systems , 2001, User modeling and user-adapted interaction.

[7]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[8]  Pierre Dragicevic,et al.  Input Device Selection and Interaction Configuration with ICON , 2001, BCS HCI/IHM.

[9]  Bracha Shapira,et al.  Recommender Systems Handbook , 2015, Springer US.

[10]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[11]  Karin Coninx,et al.  Adaptation in virtual environments: conceptual framework and user models , 2010, Multimedia Tools and Applications.

[12]  Naren Ramakrishnan,et al.  Towards Preferences in Virtual Environment Interfaces , 2002, EGVE.

[13]  Supporting user interfaces for all through user modeling , 1995 .

[14]  Ivan Poupyrev,et al.  3D User Interfaces: Theory and Practice , 2004 .

[15]  Pattie Maes,et al.  Learning Interface Agents , 1993, AAAI.

[16]  Michael J. Pazzani,et al.  Syskill & Webert: Identifying Interesting Web Sites , 1996, AAAI/IAAI, Vol. 1.

[17]  Ameet Talwalkar,et al.  Foundations of Machine Learning , 2012, Adaptive computation and machine learning.

[18]  Naren Ramakrishnan,et al.  A First Step Towards Nuance-Oriented Interfaces for Virtual Environments , 2001 .

[19]  Raimund Dachselt,et al.  Using the AMACONT architecture for flexible adaptation of 3D web applications , 2006, Web3D '06.

[20]  Georgios Paliouras,et al.  Web Usage Mining as a Tool for Personalization: A Survey , 2003, User Modeling and User-Adapted Interaction.

[21]  Chris Hand,et al.  A Survey of 3D Interaction Techniques , 1997, Comput. Graph. Forum.

[22]  Irma Lindt,et al.  Adaptive 3D-User-Interfaces , 2009 .

[23]  Joseph J. LaViola,et al.  Optimal 3D selection technique assignment using real-time contextual analysis , 2013, 2013 IEEE Symposium on 3D User Interfaces (3DUI).

[24]  Thierry Duval,et al.  Providing plasticity and redistribution for 3D user interfaces using the D3PART model , 2017, Journal on Multimodal User Interfaces.

[25]  Gaëlle Calvary.  Plasticité des Interfaces Homme-Machine: Rétrospective et Perspectives , 2010, INFORSID.

[26]  Eric Schwarzkopf.  An Adaptive Web Site for the UM2001 Conference , 2001 .

[27]  Karin Coninx,et al.  A Conceptual Framework for Adaptation and Personalization in Virtual Environments , 2009, 2009 20th International Workshop on Database and Expert Systems Application.

[28]  A. Kobsa.  User Modeling : Recent Work , Prospects and Hazards , 1993 .