Browsing by Author "Lai, Hsu-Chao"

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On VR Spatial Query for Dual Entangled Worlds
(2019-01-01)
Ko, Shao-Heng
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Lin, Ying-Chun
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Lai, Hsu-Chao
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Lee, Wang-Chien
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Yang, De-Nian
With the rapid advent of Virtual Reality (VR) technology and virtual tour applications, there is a research need on spatial queries tailored for simultaneous movements in both the physical and virtual worlds. Traditional spatial queries, designed mainly for one world, do not consider the entangled dual worlds in VR. In this paper, we first investigate the fundamental shortest-path query in VR as the building block for spatial queries, aiming to avoid hitting boundaries and obstacles in the physical environment by leveraging Redirected Walking (RW) in Computer Graphics. Specifically, we first formulate Dual-world Redirected-walking Obstacle-free Path (DROP) to find the minimum-distance path in the virtual world, which is constrained by the RW cost in the physical world to ensure immersive experience in VR. We prove DROP is NP-hard and design a fully polynomial-time approximation scheme, Dual Entangled World Navigation (DEWN), by finding Minimum Immersion Loss Range (MIL Range). Afterward, we show that the existing spatial query algorithms and index structures can leverage DEWN as a building block to support kNN and range queries in the dual worlds of VR. Experimental results and a user study with implementation in HTC VIVE manifest that DEWN outperforms the baselines with smoother RW operations in various VR scenarios.
Predicting Traffic of Online Advertising in Real-time Bidding Systems from Perspective of Demand-Side Platforms
(2016-01-01)
Lai, Hsu-Chao
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Shih, Wen-Yueh
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Huang, Jiun-Long
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Chen, Yi-Cheng
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資訊工程學系
Online advertising has been all the rage these years. Budget control and traffic prediction turn out to be important issues for the demand-side platforms (DSPs). However, DSPs cannot easily grab the information of audiences and media platforms. Although DSPs might have the information immediately, it is still hard to response the request of advertisements in real-time due to the high volume of features. Therefore, we propose a method predicting traffic of requests from perspective of DSPs. The features we used are simple to be extracted from historical data. The prediction model we chose is regression model with closed-form solution. Both the features and regression model make our prediction adaptive in real-time systems. Our method can detect traffic anomalies and prevent it from overwhelming prediction. Moreover, our method can also keep pace of the trend. Experiment results show that our method's error rate of prediction is about 0.9% in total, and 10% per time unit.
Social-Aware VR Configuration Recommendation via Multi-Feedback Coupled Tensor Factorization
(2019-01-01)
Lai, Hsu-Chao
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Shuai, Hong-Han
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Yang, De-Nian
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Huang, Jiun-Long
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Lee, Wang-Chien
Recent technological advent in virtual reality (VR) has attracted a lot of attention to the VR shopping, which thus far is designed for a single user. In this paper, we envision the scenario of VR group shopping, where VR supports: 1) flexible display of items to address diverse personal preferences, and 2) convenient view switching between personal and group views to foster social interactions. We formulate the Multiview-Enabled Configuration Recommendation (MECR) problem to rank a set of displayed items for a VR shopping user. We design the Multiview-Enabled Configuration Ranking System (MEIRS) that first extracts discriminative features based on Marketing theories and then introduces a new coupled tensor factorization model to learn the representation of users, Multi-View Display (MVD) configurations, and multiple feedback with content features. Experimental results manifest that the proposed approach outperforms personalized recommendations and group recommendations by at least 30.8% in large-scale datasets and 63.3% in the user study in terms of hit ratio and mean average precision.