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- W4313060850 abstract "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." @default.
- W4313060850 created "2023-01-06" @default.
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- W4313060850 date "2022-10-01" @default.
- W4313060850 modified "2023-10-01" @default.
- W4313060850 title "Helpfulness Prediction for VR Application Reviews: Exploring Topic Signals for Causal Inference" @default.
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- W4313060850 doi "https://doi.org/10.1109/ismar-adjunct57072.2022.00014" @default.
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