Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Optimizing and learning sequential assortment decisions with platform disengagement
Mika Sumida, Angela Zhou
University of Southern California, United States of America
We consider a problem where customers repeatedly interact with a platform. The probability that a customer engages depends on past purchase history. The platform maximizes the total revenue obtained from each customer over the horizon. We study the dynamic program when consumer preferences are known and prove structural properties. We provide a formulation in a contextual episodic RL setting and prove a regret bound. We evaluate effectiveness on simulations, using real data from Expedia.
Nested elimination: a simple algorithm for best-item identification from choice-based feedback
Junwen Yang, Yifan Feng
National University of Singapore, Singapore
In a feedback collection process, a company sequentially and adaptively shows display sets to a population of customers and collects their choices. The objective is to identify the most preferred item at a high confidence level with the least number of samples. We propose an elimination-based algorithm, namely Nested Elimination (NE). NE is intuitive, simple in structure, easy to implement, and has a strong theoretical/numerical performance for sample complexity.
