Researchers Develop Algorithm for Two-Sided Platform Matching with Unknown Customer and Seller Preferences
A new arXiv preprint introduces a data-driven algorithm for optimizing which sellers a two-sided service platform displays to customers when neither customer nor seller preferences are known in advance. The work models both sides' choices using multinomial logit models and proves the algorithm achieves polylogarithmic regret growth, with a matching lower bound confirming rate optimality. This is claimed to be the first study addressing dynamic assortment optimization when both sides' choice parameters are simultaneously unknown.
The paper, submitted to arXiv in June 2026, tackles a discrete-time dynamic assortment problem on two-sided service platforms such as gig-economy or marketplace apps. In each period, an arriving customer is shown a subset of sellers and may propose a transaction to one; sellers then review received proposals and accept at most one customer. The platform's challenge is that it must learn the multinomial logit choice parameters of both customers and sellers purely from observed interactions while simultaneously maximizing revenue. The authors develop a data-driven algorithm and evaluate it via regret—the cumulative revenue gap relative to a benchmark with full knowledge of all parameters and arrivals. They prove worst-case regret grows only polylogarithmically in the number of periods and derive a matching lower bound, establishing that no algorithm can do asymptotically better. The authors assert this is the first work to treat both sides' parameters as unknown in a dynamic assortment setting, distinguishing it from prior literature that assumes at least one side's preferences are known.
What's missing
As a preprint, the paper has not yet undergone peer review. Key open questions include how the algorithm performs empirically on real platform data, how sensitive results are to the multinomial logit assumption (which may not hold in practice), and whether the polylogarithmic regret guarantee remains tight under non-stationary customer arrival distributions or strategic seller behavior. The paper's scope is also limited to a specific cyclic timing structure for seller responses, and generalization to continuous-time or more flexible matching protocols is not addressed.
What different sources said
- arXiv cs.LGCenter
Learn to Match: Two-Sided Matching with Temporally Extended Feedback
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