Helpfulness Prediction for VR Application Reviews: Exploring Topic Signals for Causal Inference

Recently, with the development of stereo display and 3D graph-ics technology, virtual environments and applications are growing rapidly, e.g., video conferencing, and virtual reality (VR) applications. These are several E-commerce platforms that are designed for the transactions of VR applications. Consumers on these platforms can comment on a VR application after purchase. In this paper, we attempt to predict the helpfulness of these VR application reviews. To this end, we propose a topic-causal regression model that explores the influence of topic features in VR application reviews and nu-merical information on helpfulness. Specifically, we first apply the most classic topic model, Latent Dirichlet Allocation, to extract the topic signals in VR application reviews. Then, we construct a topic regression for causal inference. We perform extensive experiments on a real-world dataset collected from Oculus. The experimental results demonstrate that our model can estimate regression weights for topic factors and analyze their influence on the helpfulness of VR application reviews. © 2022 IEEE.