Decision-making from rank data is ubiquitous in our life: voters rank candidates in elections, search engines rank websites based on keywords, e-commerce websites recommend items based on users’ information and behavior. The fundamental challenge is: How can we make better decisions by learning from big ranking data?
My research tackles this multi-disciplinary challenge by a unified approach from statistics, economics, and computation. In this talk I will focus on learning. I will discuss our recent theoretical and algorithmic progresses in efficient learning of random utility models and their mixtures, which are among the most well-applied statistical models for rank data.
2017-06-14 13:00 ~ 13:45
Lirong Xia, Rensselaer Polytechnic Institute
Room 102, No.100 Wudong Road, School of Information Management & Engineering, Shanghai University of Finance & Economics