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arXiv · 2606.11118

Data-Driven Dynamic Assortment in Online Platforms: Learning about Two Sides

Rahul Roy·Nur Sunar·Jayashankar M. Swaminathan
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Abstract

We study a dynamic assortment problem on a two-sided service platform with incomplete information and heterogeneous customers in a discrete-time setting. In each period, a customer arrives seeking service, and the platform chooses an assortment of sellers to display. The customer then proposes a transaction to at most one seller in the assortment according to a multinomial logit choice model. After a fixed number of periods, sellers review the proposals they have received and each chooses at most one customer according to another multinomial logit choice model, after which the cycle repeats. A key challenge is that the platform does not know the choice-model parameters of either customers or sellers in advance. To our knowledge, this is the first study of a dynamic assortment problem in which both sides' choice parameters are unknown. We develop a data-driven algorithm that learns these parameters while optimizing the platform's objective over time. We evaluate performance using regret, which measures revenue loss relative to a clairvoyant benchmark that knows all parameters and customer arrivals in advance. We show that the algorithm's worst-case regret grows polylogarithmically over time, and we derive a matching lower bound, establishing its rate optimality.

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Rahul Roy, Nur Sunar, Jayashankar M. Swaminathan. 2026-06-10. Data-Driven Dynamic Assortment in Online Platforms: Learning about Two Sides. https://arxiv.org/abs/2606.11118

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