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English(EN) Marrying Pricing and Advertising with LLMs

大型语言模型联合优化定价和广告以增加收入

研究人员开发了一种在线 actor-critic 算法,该算法使用大型语言模型 (LLM) 来联合优化定价和广告生成。该方法将预训练 LLM 的低秩适配 (LoRA) 与需求模型相结合,以最大化卖家收入。使用合成需求模型和真实市场模拟器评估了该算法的性能,与未联合优化这些方面的基准相比,显示出显著的收入增长。 AI

影响 这项研究展示了 LLM 在电子商务中的一项新颖应用,有可能提高在线卖家的收入。

排序理由 详细介绍 LLM 新应用的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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

大型语言模型联合优化定价和广告以增加收入

本文如何被排名

Signal score
8 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
详细介绍 LLM 新应用的学术论文。[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, product
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
Same-day
Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Alessandro Barro, Francesco Bacchiocchi, Francesco Emanuele Stradi, Alberto Marchesi ·

    将定价与广告与LLMs相结合

    arXiv:2610.09985v1 Announce Type: cross Abstract: We study a sequential pricing problem in which a seller jointly posts a price and an advertisement generated by a large language model (LLM). The seller aims to maximize revenue under an unknown product demand that depends on both…