【公开学术报告】Learning Customer Preferences from Personalized Assortments

Date & Time: 9:30-11:00 am, Mon. 16^th, December 2019

Venue: Room 2101, Tongji Building A

Language: English

Speaker：Dr. Yifan FENG 冯一凡（University of Chicago）

ABSTRACT

A company wishes to commercialize a single version of a product from a menu of alternative options. Unaware of true customer preferences, the company relies on a system that allows potential buyers to provide feedback on their preferred versions. Under a general ranking-based choice model framework, we study how to dynamically individualize the set of versions shown to each customer on which they provide feedback. We prove an instance-specific lower bound on the sample complexity of any policy that identifies the top-ranked version with a given (probabilistic) confidence. We also propose a robust formulation of the company's problem and derive a sampling policy (Myopic Tracking Policy), which is both asymptotically sample optimal and intuitive to implement. We conduct computational studies on both synthetic and real-life data to assess the performance of our proposed Myopic Tracking Policy and compare it to alternative methods proposed in the literature.