【Mingli lecture 2022, Issue 25】

Speaker: Tsinghua University, Wei Qiang, tenured associate professor

Time: April 27, 2022 (Wednesday) 10:00 am

Location: Main Building 317

Introduction to the report:

The rapid development of big data and AI has greatly promoted the digitalization of management decisions. However, the incompleteness of the available data in management scenarios, the subjective disturbance of output judgment, and the complexity of the internal mechanism make the "black box" phenomenon of machine learning methods more prominent in the application of management decision-making. its in-depth application and development. In view of this research background, combined with the perspective of marketing funnel theory, and based on the situational characteristics and multi-stage dynamics of consumers' online shopping, this report proposes a recommendation method based on multi-stage dynamic Bayesian network, which can provide consumers with hidden information. Modelling and learning of the generative process of product interaction behavior driven by sexual psychological stage transfer and interest switching. This method not only has good recommendation accuracy, but also provides a solution to detect unobservable psychological stages from consumers' observable behavior, which has better management interpretability and is beneficial to designing corresponding marketing strategies.

Brief introduction of the reporter:

Dr. Wei Qiang, Deputy Director of the Department of Management Science and Engineering, School of Economics and Management, Tsinghua University, tenure-track associate professor/doctoral supervisor, Deputy Director of the Artificial Intelligence Management Research Center, and Deputy Director of the Medical Management Research Center. His research interests include management information systems, big data and business analysis, machine learning, intelligent recommendation, and text mining. He has published more than 40 papers in top journals in the field of management science and information systems (such as MISQ, ISR, INFORMS JoC, ACM TKDD).

(Organizer: Department of Management Engineering, Research and Academic Exchange Center)