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

Yang Qian; YingQiu Xiong; Yuyang Wang; Yuanchun Jiang; Yezheng Liu; Yidong Chai

doi:10.1109/VRW55335.2022.00046

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

Yang Qian
, YingQiu Xiong
, Yuyang Wang^*
, Yuanchun Jiang
, Yezheng Liu
, Yidong Chai

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

6 Citations (Scopus)

Abstract

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. © 2022 IEEE.

Original language	English
Title of host publication	Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022
Place of Publication	Los Alamitos, Calif.
Publisher	IEEE
Pages	183-188
Number of pages	6
ISBN (Electronic)	9781665484022
ISBN (Print)	9781665484039
DOIs	https://doi.org/10.1109/VRW55335.2022.00046
Publication status	Published - 2022
Externally published	Yes
Event	29th IEEE Conference on Virtual Reality and 3D User Interfaces (VR 2022) - Virtual, Christchurch, New Zealand Duration: 12 Mar 2022 → 16 Mar 2022

Publication series

Name	Proceedings - IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW

Conference

Conference	29th IEEE Conference on Virtual Reality and 3D User Interfaces (VR 2022)
Place	New Zealand
City	Christchurch
Period	12/03/22 → 16/03/22

Funding

This work is supported by the National Natural Science Foundation of China (72101072, 91846201, 72171071, 71722010), the Postdoctoral Research Foundation of China (2021M690852), the Fundamental Research Funds for the Central Universities (JZ2021HGQB0272) and the National Engineering Laboratory for Big Data Distribution and Exchange Technologies.

Research Keywords

Artificial intelligence
Computing methodologies
Feature Identification
Human computer interaction (HCI)
Human-centered computing
Interaction paradigms
Knowledge representation and reasoning
Topic Model
User Reviews
Virtual Reality
VR Game

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10.1109/VRW55335.2022.00046

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Cite this

Qian, Y., Xiong, Y., Wang, Y., Jiang, Y., Liu, Y., & Chai, Y. (2022). Identification of Key Features for VR Applications with VREVIEW: A Topic Model Approach. In Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022 (pp. 183-188). (Proceedings - IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW). IEEE. https://doi.org/10.1109/VRW55335.2022.00046

Qian, Yang ; Xiong, YingQiu ; Wang, Yuyang et al. / Identification of Key Features for VR Applications with VREVIEW : A Topic Model Approach. Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022. Los Alamitos, Calif. : IEEE, 2022. pp. 183-188 (Proceedings - IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW).

@inproceedings{3730ff053f37401dad6452294ede0dfe,

title = "Identification of Key Features for VR Applications with VREVIEW: A Topic Model Approach",

abstract = "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. {\textcopyright} 2022 IEEE.",

keywords = "Artificial intelligence, Computing methodologies, Feature Identification, Human computer interaction (HCI), Human-centered computing, Interaction paradigms, Knowledge representation and reasoning, Topic Model, User Reviews, Virtual Reality, VR Game",

author = "Yang Qian and YingQiu Xiong and Yuyang Wang and Yuanchun Jiang and Yezheng Liu and Yidong Chai",

year = "2022",

doi = "10.1109/VRW55335.2022.00046",

language = "English",

isbn = "9781665484039",

series = "Proceedings - IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW",

publisher = "IEEE",

pages = "183--188",

booktitle = "Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022",

address = "United States",

note = "29th IEEE Conference on Virtual Reality and 3D User Interfaces (VR 2022) ; Conference date: 12-03-2022 Through 16-03-2022",

}

Qian, Y, Xiong, Y, Wang, Y, Jiang, Y, Liu, Y & Chai, Y 2022, Identification of Key Features for VR Applications with VREVIEW: A Topic Model Approach. in Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022. Proceedings - IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW, IEEE, Los Alamitos, Calif., pp. 183-188, 29th IEEE Conference on Virtual Reality and 3D User Interfaces (VR 2022), Christchurch, New Zealand, 12/03/22. https://doi.org/10.1109/VRW55335.2022.00046

Identification of Key Features for VR Applications with VREVIEW: A Topic Model Approach. / Qian, Yang; Xiong, YingQiu; Wang, Yuyang et al.
Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022. Los Alamitos, Calif.: IEEE, 2022. p. 183-188 (Proceedings - IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Identification of Key Features for VR Applications with VREVIEW

T2 - 29th IEEE Conference on Virtual Reality and 3D User Interfaces (VR 2022)

AU - Qian, Yang

AU - Xiong, YingQiu

AU - Wang, Yuyang

AU - Jiang, Yuanchun

AU - Liu, Yezheng

AU - Chai, Yidong

PY - 2022

Y1 - 2022

N2 - 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. © 2022 IEEE.

AB - 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. © 2022 IEEE.

KW - Artificial intelligence

KW - Computing methodologies

KW - Feature Identification

KW - Human computer interaction (HCI)

KW - Human-centered computing

KW - Interaction paradigms

KW - Knowledge representation and reasoning

KW - Topic Model

KW - User Reviews

KW - Virtual Reality

KW - VR Game

UR - https://www.scopus.com/pages/publications/85129604177

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85129604177&origin=recordpage

U2 - 10.1109/VRW55335.2022.00046

DO - 10.1109/VRW55335.2022.00046

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781665484039

T3 - Proceedings - IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW

SP - 183

EP - 188

BT - Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022

PB - IEEE

CY - Los Alamitos, Calif.

Y2 - 12 March 2022 through 16 March 2022

ER -

Qian Y, Xiong Y, Wang Y, Jiang Y, Liu Y, Chai Y. Identification of Key Features for VR Applications with VREVIEW: A Topic Model Approach. In Proceedings - 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2022. Los Alamitos, Calif.: IEEE. 2022. p. 183-188. (Proceedings - IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW). Epub 2022 Apr 20. doi: 10.1109/VRW55335.2022.00046

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

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