Dynamic Assortment Personalization in High Dimensions
Nathan Kallus, Madeleine Udell

TL;DR
This paper addresses large-scale dynamic assortment personalization by leveraging low-rank structures to significantly reduce data requirements and improve regret bounds in high-dimensional, heterogeneous populations.
Contribution
It introduces a low-rank structured model for dynamic assortment personalization, enabling efficient learning and substantially lower regret compared to unstructured models.
Findings
Low-rank models can be learned efficiently with few interactions.
Structure-aware approaches achieve an order of magnitude lower regret.
Empirical validation confirms theoretical advantages.
Abstract
We study the problem of dynamic assortment personalization with large, heterogeneous populations and wide arrays of products, and demonstrate the importance of structural priors for effective, efficient large-scale personalization. Assortment personalization is the problem of choosing, for each individual (type), a best assortment of products, ads, or other offerings (items) so as to maximize revenue. This problem is central to revenue management in e-commerce and online advertising where both items and types can number in the millions. We formulate the dynamic assortment personalization problem as a discrete-contextual bandit with contexts (types) and exponentially many arms (assortments of the items). We assume that each type's preferences follow a simple parametric model with parameters. In all, there are parameters, and existing literature suggests that order…
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