English(EN) Bandit Profit-maximization for Targeted Marketing

新的 bandit 算法优化目标营销的利润

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

本研究论文介绍了一种在顺序营销场景中实现利润最大化的新方法。所提出的方法优化了多个具有不同需求曲线的市场中的定价和辅助变量（例如营销支出）。开发的算法专为对抗性 bandit 设置而设计，旨在最小化需求曲线任意且仅观察到噪声评估的场景中的遗憾。 AI

影响在顺序需求场景中引入了优化营销支出和定价的新算法。

排序理由该集群包含一篇发表在 arXiv 上的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 34 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇发表在 arXiv 上的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

arXiv cs.LG TIER_1 English(EN) · Joon Suk Huh, Ellen Vitercik, Kirthevasan Kandasamy · 2026-11-11 05:00

Bandit 利润最大化用于定向营销

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…