报告人： 谢佳亨助理教授  University of Delaware

时间：2022年5月16日上午9:00-10:30

腾讯会议号：929 814 766

报告内容简介：

As video-sharing shapes an emerging social media landscape, content creators and businesses urge to prioritize video viewership prediction to optimize influence and marketing outreach with minimum budgets. Although deep learning champions viewership prediction, it lacks interpretability, which is required by regulators and is fundamental to guiding video production and accepting predictive models. Existing interpretable predictive models face the challenges of imprecise interpretation and negligence of unstructured data. Following the design-science paradigm, we propose a novel information system, Precise Wide-and-Deep Learning (PrecWD), that accurately predicts viewership leveraging unstructured raw videos and well-established features while precisely interpreting feature effects. PrecWD outperforms benchmarks in two contexts – health video and misinformation viewership prediction – and achieves superior interpretability in a user study. We contribute to IS knowledge base by enabling precise interpretability in video-based predictive analytics. We also contribute to IS design theory with generalizable design principles in model development. Our system and findings are deployable to improve video-based social media presence.

报告人简介：

谢佳亨博士是特拉华大学阿尔弗雷德·勒纳商学院会计与管理信息系统系助理教授。他在亚利桑那大学埃勒12BET获得博士学位。他的研究兴趣包括深度学习、健康风险分析和商业分析。他之前的工作曾在许多重要期刊上发表，包括MIS Quarterly, Journal of Management Information Systems, and Journal of American Medical Informatics Association。

（承办：管理工程系、科研与学术交流中心）