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New bandit algorithms optimize profit for targeted marketing

By PulseAugur Editorial · [1 sources] ·

This research paper introduces a new approach to profit maximization in sequential marketing scenarios. The proposed method optimizes both pricing and ancillary variables, such as marketing expenditures, across multiple markets with varying demand curves. The algorithms developed are designed for an adversarial bandit setting, aiming to minimize regret in scenarios where demand curves are arbitrary and only noisy evaluations are observed. AI

IMPACT Introduces novel algorithms for optimizing marketing spend and pricing in sequential demand scenarios.

RANK_REASON The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

New bandit algorithms optimize profit for targeted marketing

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34 / 100
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The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Joon Suk Huh, Ellen Vitercik, Kirthevasan Kandasamy ·

    Bandit Profit-maximization for Targeted Marketing

    arXiv:2403.01361v3 Announce Type: replace Abstract: We study a sequential profit-maximization problem, optimizing for both price and ancillary variables like marketing expenditures. Specifically, we aim to maximize profit over an arbitrary sequence of multiple demand curves, each…

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