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

Source: arXiv - 2606.11118v1

Overview

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.

Key Contributions

This paper presents research in the following areas:

• cs.LG
• math.OC
• math.PR
• stat.AP
• stat.ML

Methodology

Please refer to the full paper for detailed methodology.

Practical Implications

This research contributes to the advancement of cs.LG.

Authors

• Rahul Roy
• Nur Sunar
• Jayashankar M. Swaminathan

Paper Information

• arXiv ID: 2606.11118v1
• Categories: cs.LG, math.OC, math.PR, stat.AP, stat.ML
• Published: June 9, 2026
• PDF: Download PDF