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Identification of Key Features for VR Applications with VREVIEW: A Topic Model Approach

Communication avec acte
Auteur
QIAN, Yang
493621 Hefei University of Technology [HFUT]
XIONG, YingQiu
493621 Hefei University of Technology [HFUT]
ccWANG, Yuyang
543315 Laboratoire d’Ingénierie des Systèmes Physiques et Numériques [LISPEN]
JIANG, Yuanchun
493621 Hefei University of Technology [HFUT]
LIU, Yezheng
493621 Hefei University of Technology [HFUT]
CHAI, Yidong
493621 Hefei University of Technology [HFUT]

URI
http://hdl.handle.net/10985/21879
DOI
10.1109/vrw55335.2022.00046
Date
2022-03-12

Résumé

These are a series of online platforms that allow users to rate and comment on VR virtual reality applications. In this paper, we develop a topic model, namely the general and sparse topic model, that automatically identifies a set of features of VR applications from user reviews. In our context, we overcome two severe challenges (i.e., internal noise and limited features mentioned in each review) to successfully learn the features of VR applications. Specifically, we introduce a general topic and a “spike and slab” prior. In addition, we design a collapsed Gibbs sampling algorithm for model inference. We apply this topic model to a dataset from Oculus (namely VREVIEW), and show that our model can identify some distinct, economically meaningful features for VR applications, e.g., “entertainment and fun,” “challenge,” “immersive,” and “sickness.” Our research provides implications for VR consumer behavior analysis, optimizing user experience in virtual environments, and VR application recommendation.

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