Papers Assortment Optimization
“Assortment Optimization” 태그가 달린 논문 32편 · 필터 해제
Assortment Optimization for Patient-Provider Matching
Rising provider turnover results in frequently needing to rematch patients with available providers. However, the rematching process is cumbersome for both patients and health systems, resulting in labor-intensive and ad…
Assortment OptimizationLearning an Optimal Assortment Policy under Observational Data
We study the fundamental problem of offline assortment optimization under the Multinomial Logit (MNL) model, where sellers must determine the optimal subset of the products to offer based solely on historical customer ch…
Assortment OptimizationOn Pareto Optimality for the Multinomial Logistic Bandit
We provide a new online learning algorithm for tackling the Multinomial Logit Bandit (MNL-Bandit) problem. Despite the challenges posed by the combinatorial nature of the MNL model, we develop a novel Upper Confidence Bo…
Assortment Optimizationparameter estimationConsumer Transactions Simulation through Generative Adversarial Networks
In the rapidly evolving domain of large-scale retail data systems, envisioning and simulating future consumer transactions has become a crucial area of interest. It offers significant potential to fortify demand forecast…
Assortment OptimizationDemand ForecastingOuter Approximation and Super-modular Cuts for Constrained Assortment Optimization under Mixed-Logit Model
In this paper, we study the assortment optimization problem under the mixed-logit customer choice model. While assortment optimization has been a major topic in revenue management for decades, the mixed-logit model is co…
Assortment OptimizationManagementvalidA Re-solving Heuristic for Dynamic Assortment Optimization with Knapsack Constraints
In this paper, we consider a multi-stage dynamic assortment optimization problem with multi-nomial choice modeling (MNL) under resource knapsack constraints. Given the current resource inventory levels, the retailer make…
Assortment OptimizationStop Relying on No-Choice and Do not Repeat the Moves: Optimal, Efficient and Practical Algorithms for Assortment Optimization
We address the problem of active online assortment optimization problem with preference feedback, which is a framework for modeling user choices and subsetwise utility maximization. The framework is useful in various rea…
Assortment OptimizationRecommendation SystemsNon-monotone Sequential Submodular Maximization
In this paper, we study a fundamental problem in submodular optimization, which is called sequential submodular maximization. Specifically, we aim to select and rank a group of $k$ items from a ground set $V$ such that t…
Assortment OptimizationDiversityRecommendation SystemsA Neural Network Based Choice Model for Assortment Optimization
Discrete-choice models are used in economics, marketing and revenue management to predict customer purchase probabilities, say as a function of prices and other features of the offered assortment. While they have been sh…
Assortment OptimizationDiscrete Choice ModelsMarketingA Model of Competitive Assortment Planning Algorithm
With a novel search algorithm or assortment planning or assortment optimization algorithm that takes into account a Bayesian approach to information updating and two-stage assortment optimization techniques, the current …
Assortment OptimizationmodelPASTA: Pessimistic Assortment Optimization
We consider a class of assortment optimization problems in an offline data-driven setting. A firm does not know the underlying customer choice model but has access to an offline dataset consisting of the historically off…
Assortment OptimizationConsider or Choose? The Role and Power of Consideration Sets
Consideration sets play a crucial role in discrete choice modeling, where customers often form consideration sets in the first stage and then use a second-stage choice mechanism to select the product with the highest uti…
Assortment OptimizationDecision MakingMarketingCombinatorial Inference on the Optimal Assortment in Multinomial Logit Models
Assortment optimization has received active explorations in the past few decades due to its practical importance. Despite the extensive literature dealing with optimization algorithms and latent score estimation, uncerta…
Assortment OptimizationChange Point DetectionUncertainty QuantificationvalidA Nonparametric Approach with Marginals for Modeling Consumer Choice
Given data on the choices made by consumers for different offer sets, a key challenge is to develop parsimonious models that describe and predict consumer choice behavior while being amenable to prescriptive tasks such a…
Assortment OptimizationPrediction IntervalsIdentifying Substitute and Complementary Products for Assortment Optimization with Cleora Embeddings
Recent years brought an increasing interest in the application of machine learning algorithms in e-commerce, omnichannel marketing, and the sales industry. It is not only to the algorithmic advances but also to data avai…
Assortment OptimizationGraph EmbeddingMarketingAssortment Optimization with Customer Choice Modeling in a Crowdfunding Setting
Crowdfunding, which is the act of raising funds from a large number of people's contributions, is among the most popular research topics in economic theory. Due to the fact that crowdfunding platforms (CFPs) have facilit…
Assortment OptimizationThe Limits of Personalization in Assortment Optimization
To study the limits of personalization, we introduce the notion of a clairvoyant firm that can read the mind of consumers and sell them the highest revenue product that they are willing to buy. We show how to compute the…
Assortment OptimizationDiscrete Choice ModelsThe Refined Assortment Optimization Problem
We introduce the refined assortment optimization problem where a firm may decide to make some of its products harder to get instead of making them unavailable as in the traditional assortment optimization problem. Airlin…
Assortment OptimizationDiscrete Choice ModelsAdversarial Combinatorial Bandits with General Non-linear Reward Functions
In this paper we study the adversarial combinatorial bandit with a known non-linear reward function, extending existing work on adversarial linear combinatorial bandit. {The adversarial combinatorial bandit with general …
Assortment OptimizationThe Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems
Online allocation problems with resource constraints are central problems in revenue management and online advertising. In these problems, requests arrive sequentially during a finite horizon and, for each request, a dec…
Assortment OptimizationManagement