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New algorithm tackles censored demand for online retail pricing and inventory

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new algorithm called Mean-Calibrated Kernel UCB (MCK-UCB) to address challenges in online retail pricing and inventory management. This algorithm learns optimal policies even when demand data is incomplete due to stockouts, a common issue in e-commerce. MCK-UCB uses past sales data from similar market conditions to make informed decisions about pricing and stocking, allowing for continuous learning without separate exploration phases. The algorithm has been proven to be minimax optimal and demonstrates strong performance in numerical experiments. AI

IMPACT This research could lead to more efficient inventory management and pricing strategies in e-commerce, optimizing profits and customer satisfaction.

RANK_REASON The cluster contains a research paper detailing a new algorithm for a specific problem domain. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New algorithm tackles censored demand for online retail pricing and inventory

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The cluster contains a research paper detailing a new algorithm for a specific problem domain. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zean Han, Jing Liang, Ruihan Lin, Zezhen Ding, Jiheng Zhang ·

    Nonparametric Contextual Pricing and Inventory Learning under Censored Demand

    arXiv:2608.30944v1 Announce Type: new Abstract: In online retailing, when a product sells out, a retailer often sees only the units sold, not how many customers would have bought it had inventory been available. However, the inventory level determines how much demand is revealed,…

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