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數(shù)據(jù)科學(xué)與管理工程學(xué)系學(xué)術(shù)講座

【主講題目】Robust Learning of Consumer Preferences

【主講人】馮一凡，Assistant Professor，NUS Business School

講座信息

時(shí)間：2021年12月8日（周三）上午09:30 –10:30

參與方式：線上騰訊會(huì)議（ID : 574 920 388，密碼：202112）

主講人簡介

Yifan Feng is an Assistant Professor at the Department of Analytics and Operations (DAO), NUS Business School. Broadly, he is interested in prescriptive analytics for platforms and markets. Specifically, he is interested in stochastic modeling with applications to information aggregation and acquisition, experimentation, and demand fulfillment. Prior to NUS, Yifan obtained his Ph.D. degree from the University of Chicago Booth School of Business.

講座摘要

A company wants to identify the most preferred product out of a finite set of alternatives when consumer preferences are unknown. It is able to sample consumer preferences by sequentially showing different subsets of products to different consumers and asking them to report their top preferences within the displayed set. The company aims to design a display policy that minimizes the expected number of samples needed to identify the top-ranked product with high probability.

Using a sequential hypothesis testing framework, we prove an instance-specific lower bound on the sample complexity of any policy that identifies the top-ranked version within a given (probabilistic) confidence. We also propose a robust formulation of the company's problem and derive a sampling policy, which is both worst-case asymptotically optimal and intuitive to implement. Roughly speaking, it is a delicate balance among display sets to maximize the informativeness of choices made by the consumers.（This is joint work with René Caldentey and Christopher T. Ryan.）

信息來源：浙大管院數(shù)據(jù)科學(xué)與管理工程學(xué)系

今日編輯/排版：段婷

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