Data Monetization with Machine Learning
Time：12th Nov, 2019, 13:30pm
Location：Room 314
Reporter：Fei TAN
Bio: Fei TAN，received his PH.D in New Jersey Institute of Technology in May,2019, and his thesis is awarded Joseph Leung prize。He is now a researcher in Yahoo Labs of Verizon Media Group in the USA.His research interests including data mining, machine learning, and his publications mainly on IEEE TNNLS (Influence factor: 11.68), IEEE ICDM, SIAM SDM, IJCAI, Data Mining and Knowledge Discovery, Physical Review E, Euro physics Letters, et al.
Abstract: Machine learning has being harnessed to refine big data and render it value like never before. In this talk, we will explain three data monetization cases through machine learning. Specifically, in online lending, how to represent two competing risks, charge-off and prepayment, in funded loans is a fundamental problem behind ROI maximization. We develop a hierarchical grading framework to integrate them both qualitatively and quantitatively. In addition, in digital marketing, we propose to treat content understanding as to elucidate their causal implications in driving user responses. A flexible and adaptive doubly robust estimator is introduced to identify the causality between related features and user responses from observational data. In online communities, abusive language has profound impacts on their integrity. We explore two byte-level quantization schemes for character representation. The primitive representation empowers models to capture signals underlying multi-byte characters of online posts elegantly.
