報告人： 謝佳亨助理教授 University of Delaware
時間：2022年5月16日上午9:00-10:30
騰訊會議號：929 814 766
報告內(nèi)容簡介：
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.
報告人簡介：
謝佳亨博士是特拉華大學(xué)阿爾弗雷德·勒納商學(xué)院會計與管理信息系統(tǒng)系助理教授。他在亞利桑那大學(xué)埃勒偉德國際1946bv官網(wǎng)獲得博士學(xué)位。他的研究興趣包括深度學(xué)習(xí)、健康風(fēng)險分析和商業(yè)分析。他之前的工作曾在許多重要期刊上發(fā)表，包括MIS Quarterly, Journal of Management Information Systems, and Journal of American Medical Informatics Association。
（承辦：管理工程系、科研與學(xué)術(shù)交流中心）
