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Exploration Optimization for Dynamic Assortment Personalization under Linear Preferences

https://doi.org/10.2139/ssrn.4115721
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19/19 checkable references clean · checked 2026-10-16

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

14 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 19 checked references that resolve
resolves10.1287/opre.2018.1832
MNL-Bandit: A Dynamic Learning Approach to Assortment Selection
resolves10.1287/mnsc.2020.3605
Mostly Exploration-Free Algorithms for Contextual Bandits
resolves10.1287/msom.2015.0544
Dynamic Assortment Customization with Limited Inventories
resolves10.1111/poms.12402
Product Assortment and Price Competition under Multinomial Logit Demand <sup/>
resolves10.1287/opre.1080.0640
Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms
resolves10.1137/141000671
Julia: A Fresh Approach to Numerical Computing
resolves10.1287/mnsc.1060.0613
Dynamic Assortment with Demand Learning for Seasonal Consumer Goods
resolves10.1287/mnsc.46.2.327.11931
Technical Note: Mathematical Properties of the Optimal Product Line Selection Problem Using Choice-Based Conjoint Analysis
resolves10.1145/2528404
Fast matrix rank algorithms and applications
resolves10.1287/opre.2017.1629
Technical Note—Dynamic Pricing and Demand Learning with Limited Price Experimentation
resolves10.1287/mnsc.2021.4130
Learning to Rank an Assortment of Products
resolves10.1287/opre.2018.1755
Online Network Revenue Management Using Thompson Sampling
resolves10.1287/mnsc.2014.1904
A Demand Estimation Procedure for Retail Assortment Optimization with Results from Implementations
resolves10.1287/mnsc.2014.1939
Real-Time Optimization of Personalized Assortments
resolves10.1287/mnsc.1110.1426
Bayesian Dynamic Pricing Policies: Learning and Earning Under a Binary Prior Distribution
resolves10.1287/opre.2019.1948
Dynamic Assortment Personalization in High Dimensions
resolves10.1016/0196-8858(85)90002-8
Asymptotically efficient adaptive allocation rules
resolves10.1016/j.dam.2012.03.003
A branch-and-cut algorithm for the latent-class logit assortment problem
resolves10.1287/opre.2019.1926
Learning in Combinatorial Optimization: What and How to Explore
The 14 references without a DOI — listed, not checked
no DOI — not checkedImproved algorithms for linear stochastic bandits
no DOI — not checkedThompson sampling for contextual bandits with linear payoffs
no DOI — not checkedA dynamic clustering approach to data-driven assortment personalization
no DOI — not checkedref10
no DOI — not checkedA statistical learning approach to personalization in revenue management
no DOI — not checkedDynamic assortment optimization with changing contextual information
no DOI — not checkedThompson sampling for online personalized assortment optimization problems with multinomial logit choice models
no DOI — not checkedStochastic linear optimization under bandit feedback
no DOI — not checkedParametric bandits: The generalized linear case
no DOI — not checkedref23
no DOI — not checkedAssortment planning: Review of literature and industry practice
no DOI — not checkedref28
no DOI — not checkedProvably optimal algorithms for generalized linear contextual bandits
no DOI — not checkedref32
What this badge says. CiteStamped means the CHECKABLE references of this work were clean at the dated check: each resolved to a known work in a public registry, and none carried a retraction notice at that time. It says nothing about the quality, findings, or importance of the work itself, and nothing about references deposited without a DOI.

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