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

• URL: http://arxiv.org/abs/2606.11118v2
• Date: Wed, 10 Jun 2026 06:30:43 GMT
• Title: Data-Driven Dynamic Assortment in Online Platforms: Learning about Two Sides
• Abstract summary: We study a dynamic assortment problem on a two-sided service platform with incomplete information and heterogeneous customers.<n>A key challenge is that the platform does not know the choice-model parameters of either customers or sellers in advance.<n>We develop a data-driven algorithm that learns these parameters while optimizing the platform's objective over time.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• SalesSim: Benchmarking and Aligning Multimodal Language Models as Retail User Simulators [63.68151307455963]
  We present SalesSim, a framework and testbed for evaluating the ability of Multimodal Large Language Models (MLLMs) to simulate realistic, persona-driven customer behavior.<n>We measure consistency between the simulator's actions and its persona specifications, as well as conversational quality.<n>Our experiments demonstrate that UserGRPO boosts decision alignment of the baseline model by 13.8% while improving conversational quality.
  arXiv  Detail & Related papers  (2026-05-08T17:59:23Z)
• Poisson-MNL Bandit: Nearly Optimal Dynamic Joint Assortment and Pricing with Decision-Dependent Customer Arrivals [2.023747761104056]
  We study dynamic joint assortment and pricing where a seller updates decisions at regular accounting/operating intervals.<n>In many settings, assortment and prices affect not only what an arriving customer buys but also how many customers arrive within the period.<n>We propose a Poisson-MNL model that couples a contextual MNL choice model with a Poisson arrival model whose rate depends on the offered assortment and prices.
  arXiv  Detail & Related papers  (2026-02-18T22:30:49Z)
• RIFLES: Resource-effIcient Federated LEarning via Scheduling [4.358456799125694]
  Federated Learning (FL) is a privacy-preserving machine learning technique that allows decentralized collaborative model training across a set of distributed clients.<n>Current selection strategies are myopic in nature in that they are based on past or current interactions.<n>RIFLES builds a novel availability forecasting layer to support the client selection process.
  arXiv  Detail & Related papers  (2025-05-19T14:26:33Z)
• An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting [53.36437745983783]
  We first construct a max-margin optimization-based model to model potentially non-monotonic preferences. We devise information amount measurement methods and question selection strategies to pinpoint the most informative alternative in each iteration. Two incremental preference elicitation-based algorithms are developed to learn potentially non-monotonic preferences.
  arXiv  Detail & Related papers  (2024-09-04T14:36:20Z)
• A Primal-Dual Online Learning Approach for Dynamic Pricing of Sequentially Displayed Complementary Items under Sale Constraints [54.46126953873298]
  We address the problem of dynamically pricing complementary items that are sequentially displayed to customers. Coherent pricing policies for complementary items are essential because optimizing the pricing of each item individually is ineffective. We empirically evaluate our approach using synthetic settings randomly generated from real-world data, and compare its performance in terms of constraints violation and regret.
  arXiv  Detail & Related papers  (2024-07-08T09:55:31Z)
• Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning [50.060154488277036]
  In federated learning, client selection is a critical problem that significantly impacts both model performance and fairness.<n>We propose two guiding principles that tackle the inherent conflict between the two metrics while reinforcing each other.<n>Our approach adaptively enhances this diversity by selecting clients based on their data distributions, thereby improving both model performance and fairness.
  arXiv  Detail & Related papers  (2024-05-22T12:27:24Z)
• Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service [17.986744632466515]
  Federated Learning (FL) enables multiple clients to train machine learning models collaboratively without sharing the raw training data. We propose a multi-criteria client selection and scheduling scheme with a fairness guarantee. Our scheme can improve the model quality especially when data are non-iid.
  arXiv  Detail & Related papers  (2023-12-05T16:56:24Z)
• Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation [127.35910314813854]
  We present the Amazon Multi-locale Shopping Session dataset, namely Amazon-M2. It is the first multilingual dataset consisting of millions of user sessions from six different locales. Remarkably, the dataset can help us enhance personalization and understanding of user preferences.
  arXiv  Detail & Related papers  (2023-07-19T00:08:49Z)
• Online Joint Assortment-Inventory Optimization under MNL Choices [11.669431684184534]
  We study an online joint assortment-inventory optimization problem, in which we assume that the choice behavior of each customer follows the Multinomial Logit (MNL) choice model.<n>We propose a novel algorithm that can effectively balance exploration and exploitation in the online decision-making of assortment and inventory.
  arXiv  Detail & Related papers  (2023-04-04T09:25:34Z)
• Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation [55.0391061198924]
  Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence. It is nontrivial to incorporate price preferences for session-based recommendation. We propose a novel method Co-guided Heterogeneous Hypergraph Network (CoHHN) for session-based recommendation.
  arXiv  Detail & Related papers  (2022-05-09T10:47:15Z)
• Optimizing Offer Sets in Sub-Linear Time [5.027714423258537]
  We propose an algorithm for personalized offer set optimization that runs in time sub-linear in the number of items. Our algorithm can be entirely data-driven, relying on samples of the user, where a sample' refers to the user interaction data typically collected by firms.
  arXiv  Detail & Related papers  (2020-11-17T13:02:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.