| Session | ||
MC1 - AI8: Learning the best choice
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| Presentations | ||
Optimizing and learning sequential assortment decisions with platform disengagement 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 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. | ||