Business Performance Prediction in Location-Based Social Commerce
主讲人：Xiaohui Chang, Oregon State University
Social commerce and location-based services provide a data platform for coexisting and competing businesses in geographical neighborhoods. Our research is aimed at mining data from such platforms to gain valuable insights for better support for strategic and operational business decisions. We develop a computational framework for predicting business performance that takes into account both intrinsic (e.g., attributes) and extrinsic (e.g., competitions) factors. Our experiments on synthetic and real datasets demonstrated the superiority of a hybrid prediction model that adopts both link-based and context-based assumptions.
Xiaohui Chang is an Assistant Professor of Business Analytics at the College of Business, Oregon State University. She holds a Ph.D. in Statistics from the University of Chicago and earned B.A. (Honors) in Statistics and B.A. in Economics from the University of Chicago. Her research interests include machine learning, spatial statistics, location-based services, mHealth, and business performance prediction. Her works have been published in Quantitative Finance, IEEE Transactions, Biometrics, and Computation Statistics and Data Analysis.