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.
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References
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152. ACM (1992)
Bowman, D.A., Kruijff, E., LaViola, J.J., Poupyrev, I.: 3D User Interfaces: Theory and Practice. Addison Wesley Longman Publishing Co. Inc., Redwood City (2004)
Calvary, G.: Plasticité des Interfaces Homme-Machine. Ph.D. thesis, thèse Habilitation à Diriger des Recherches préparée au Laboratoire d’Informatique de Grenoble (LIG), Université Joseph Fourier (2007)
Cashion, J., Wingrave, C., LaViola, J.: Optimal 3D selection technique assignment using real-time contextual analysis. In: 2013 IEEE Symposium on 3D User Interfaces (3DUI), pp. 107–110, March 2013
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)
Chittaro, L., Ranon, R., Artificial, I.S., Realities, V.: Dynamic generation of personalized VRML content: a general approach and its application to 3D e-commerce. In: Proceedings of Web3D 2002: 7th International Conference on 3D Web, pp. 145–154. Press (2002)
Dachselt, R., Hinz, M., Pietschmann, S.: Using the AMACONT architecture for flexible adaptation of 3D web applications. In: Proceedings of the Eleventh International Conference on 3D Web Technology, Web3D 2006, pp. 75–84. ACM, New York (2006). https://doi.org/10.1145/1122591.1122602. http://doi.acm.org/10.1145/1122591.1122602
Dragicevic, P., Fekete, J.D.: Input device selection and interaction configuration with ICON. In: Blandford, A., Vanderdonckt, J., Gray, P. (eds.) People and Computers XV—Interaction without Frontiers, pp. 543–558. Springer, London (2001). https://doi.org/10.1007/978-1-4471-0353-0_34
Fischer, G.: User modeling in human-computer interaction. User Model. User-Adap. Interact. 11(1–2), 65–86 (2001)
Hand, C.: A survey of 3D interaction techniques. In: Computer Graphics Forum, vol. 16, pp. 269–281. Wiley Online Library (1997)
Kobsa, A.: User modeling: recent work, prospects and hazards. Hum. Factors Inf. Technol. 10, 111 (1993)
Kobsa, A.: Supporting user interfaces for all through user modeling. Adv. Hum. Factors/Ergon. 20, 155–157 (1995)
Kobsa, A.: Generic user modeling systems. User Model. User-Adap. Interact. 11(1–2), 49–63 (2001)
Lacoche, J., Duval, T., Arnaldi, B., Maisel, É., Royan, J.: Providing plasticity and redistribution for 3D user interfaces using the D3PART model. J. Multimodal User Interfaces 11(2), 197–210 (2017)
Lindt, I.: Adaptive 3D-user-interfaces. Ph.D. thesis (2009)
Maes, P., Kozierok, R.: Learning interface agents. In: Proceedings of the Eleventh National Conference on Artificial Intelligence, AAAI 1993, pp. 459–464. AAAI Press (1993)
Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of Machine Learning. MIT Press, Cambridge (2012)
Octavia, J.R., Raymaekers, C., Coninx, K.: A conceptual framework for adaptation and personalization in virtual environments. In: 20th International Workshop on Database and Expert Systems Application, DEXA 2009, pp. 284–288 (2009)
Octavia, J., Raymaekers, C., Coninx, K.: Adaptation in virtual environments: conceptual framework and user models. Multimed. Tools Appl. 54(1), 121–142 (2011). https://doi.org/10.1007/s11042-010-0525-z
Pazzani, M.J., Muramatsu, J., Billsus, D., et al.: Syskill & webert: identifying interesting web sites. In: AAAI/IAAI, vol. 1, pp. 54–61 (1996)
Pierce, J.S., Forsberg, A.S., Conway, M.J., Hong, S., Zeleznik, R.C., Mine, M.R.: Image plane interaction techniques in 3D immersive environments. In: Proceedings of the 1997 Symposium on Interactive 3D Graphics, pp. 39-ff. ACM (1997)
Pierrakos, D., Paliouras, G., Papatheodorou, C., Spyropoulos, C.D.: Web usage mining as a tool for personalization: a survey. User Model. User-Adap. Interact. 13(4), 311–372 (2003)
Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, Boston, MA (2011). https://doi.org/10.1007/978-0-387-85820-3_1
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook. Springer, Heidelberg (2015). https://doi.org/10.1007/978-1-4899-7637-6
Schwarzkopf, E.: An adaptive web site for the UM2001 conference. In: Proceedings of the UM2001 Workshop on Machine Learning for User Modeling, pp. 77–86 (2001)
Soukoreff, R.W., MacKenzie, I.S.: Towards a standard for pointing device evaluation, perspectives on 27 years of Fitts’ law research in HCI. Int. J. Hum.-Comput. Stud. 61(6), 751–789 (2004)
Wingrave, C.A., Bowman, D.A., Ramakrishnan, N.: A first step towards nuance-oriented interfaces for virtual environments (2001)
Wingrave, C.A., Bowman, D.A., Ramakrishnan, N.: Towards preferences in virtual environment interfaces. In: Proceedings of the Workshop on Virtual Environments, EGVE 2002, pp. 63–72. Eurographics Association, Aire-la-Ville (2002). http://dl.acm.org/citation.cfm?id=509709.509720
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Lacoche, J., Duval, T., Arnaldi, B., Maisel, E., Royan, J. (2019). Machine Learning Based Interaction Technique Selection for 3D User Interfaces. In: Bourdot, P., Interrante, V., Nedel, L., Magnenat-Thalmann, N., Zachmann, G. (eds) Virtual Reality and Augmented Reality. EuroVR 2019. Lecture Notes in Computer Science(), vol 11883. Springer, Cham. https://doi.org/10.1007/978-3-030-31908-3_3
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