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SUMMARY:How to Model Buyer Preferences to Maximize Revenues?
DTSTART:20140714T110000
DTEND:20140714T120000
DTSTAMP:20260506T015745Z
UID:40f145a3813f63936f0efdc545a168215cc6f5f73780cbc9e08e32c3
CATEGORIES:Conferences - Seminars
DESCRIPTION:Vineet Goyal\, Columbia University\nAbstract: Consider a selle
 r with n (substitutable) products with fixed prices and unlimited supply. 
 Each buyer has a random preference (a permutation over products) and selec
 ts the most preferred product among the ones offered by the seller. The se
 ller needs to decide on the subset of products to offer such that expected
  revenue over random preferences is maximized. This problem is referred to
  as the assortment planning problem in the literature and arises in many a
 pplications including retailing and airlines.\nThe two fundamental challen
 ges in the assortment planning problem are: i) constructing a model for bu
 yer preferences from historical data that reveals only selections and not 
 preferences\, and ii) solving the resulting assortment optimization proble
 m efficiently. In this paper\, we present a new paradigm for modeling buye
 r preferences that gives a simultaneous approximation for a large family o
 f parametric preference models. Moreover\, the resulting assortment optimi
 zation problem is poly-time solvable. In this talk\, I will discuss the pr
 operties of this new preference model and conclude with several open chall
 enges in this area. The talk should be accessible to a broad audience.\nBi
 o: Professor Vineet Goyal joined the Industrial Engineering and Operations
  Research Department in 2010. He received his Bachelor's degree in Compute
 r Science from Indian Institute of Technology\, Delhi in 2003 and his Ph.D
 . in Algorithms\, Combinatorics and Optimization (ACO) from Carnegie Mello
 n University in 2008. Before coming to Columbia\, he spent two years as a 
 Postdoctoral Associate at the Operations Research Center at MIT. Professor
  Goyal is interested in the design of efficient and robust data-driven alg
 orithms for large scale dynamic optimization problems with applications in
  energy markets and revenue management problems. His research has been con
 tinually supported by grants from NSF and industry. He received the NSF CA
 REER Award in 2014 and a Google Faculty Research Award in 2013.
LOCATION:BC 420 https://plan.epfl.ch/?room==BC%20420
STATUS:CONFIRMED
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