[Ml-stat-talks] Princeton Optimization Seminar: Vineet Goyal, Thu. TODAY, 4:30 PM

Amir Ali Ahmadi a_a_a at princeton.edu
Thu Mar 31 08:50:02 EDT 2016

-----   Princeton Optimization Seminar   -----


TIME:  4:30 PM

LOCATION:  Sherrerd Hall 101

SPEAKER: Vineet Goyal, Columbia University

TITLE: A Markovian Approach to Choice Modeling and Assortment Optimization<https://orfe.princeton.edu/abstracts/optimization-seminar/markovian-approach-choice-modeling-and-assortment-optimization>

Assortment optimization is an important problem that arises in many practical applications such as retailing and online advertising where the goal is to select a subset of items to offer from a universe of substitutable items such that the expected revenue is maximized. One of the fundamental challenges in these problems is to identify a ``good'' model for customer preferences and substitution behavior especially since the preferences are latent and not observable in data.
We consider a Markovian approach to address the model selection problem where substitutions are modeled as state transitions in a Markov chain. We show that this model provides a good approximation for all random utility based discrete choice models including the multinomial logit, the nested logit and mixtures of multinomial logit models, thereby addressing the model selection problem.
Furthermore, we present an efficient iterative algorithm for assortment optimization under the Markov chain model that gives an optimal for the unconstrained version and an approximation for the capacity constrained version. Our algorithm is based on a ``local-ratio" paradigm that allows us to linearize the revenue function and provides a framework to capture the externality of our actions on the residual instance in each iteration. This also provides interesting insights for the assortment optimization problem over other choice models.

Vineet Goyal is Associate Professor in the Department of Industrial Engineering and Operations Research in the School of Engineering and Applied Sciences, Columbia University. He received his Bachelor's degree in Computer Science from Indian Institute of Technology, Delhi in 2003 and his Ph.D. in Algorithms, Combinatorics and Optimization (ACO) from Carnegie Mellon University in 2008. Before coming to Columbia, he spent two years as a Postdoctoral Associate at the Operations Research Center at MIT.
He is interested in the design of efficient and robust data-driven algorithms for large scale dynamic optimization problems with a focus on applications in pricing, revenue management and energy markets. His research has been continually supported by grants from NSF and industry. He received the NSF CAREER Award in 2014, IBM Faculty Award in 2014 and a Google Faculty Research Award in 2013.

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