带预算和ROI约束的多渠道自动出价
Multi-channel Autobidding with Budget and ROI Constraints
AI总结：
针对多渠道广告投放中满足总体预算与ROI约束下最大化转化量的问题，分析单渠道ROI与预算两种调控手段的效果，提出基于老虎机反馈的单渠道预算优化学习算法，可逼近全局最优转化量，且结果适用于单/多品拍卖场景。
摘要：
在数字在线广告中，广告主同时在多个平台（即所谓的渠道，如Google Ads、Meta Ads Manager等）上获取广告曝光，每个渠道都包含大量广告拍卖。我们研究广告主如何在满足所有渠道的总体投资回报率（ROI）和预算约束的前提下，最大化总转化量（例如广告点击量）。在实践中，广告主无法控制、因而也无法全局优化自己在每个渠道中参与哪些单独的广告拍卖，而是授权渠道代其获取曝光：广告主在每个渠道上只能使用两个调控手段，即设置单渠道预算和单渠道目标ROI。在本研究中，我们首先分析了这两种调控手段各自对于解决广告主的全局多渠道问题的有效性。我们证明，当广告主仅优化单渠道ROI时，其总转化量可能远低于她在全局问题中本可以获得的水平。此外，我们证明，当广告主仅优化单渠道预算时，她可以实现全局最优转化量。基于这一发现，在模拟现实场景的老虎机反馈设置下（即广告主对每个渠道的广告拍卖情况以及渠道如何获取广告的信息有限），我们提出了一种高效的学习算法，该算法生成的单渠道预算所带来的转化量可逼近全局最优问题的转化量。最后，我们指出，我们的所有结果对于渠道代广告主获取曝光的单品拍卖和多品拍卖均成立。
英文摘要：
In digital online advertising, advertisers procure ad impressions simultaneously on multiple platforms, or so-called channels, such as Google Ads, Meta Ads Manager, etc., each of which consists of numerous ad auctions. We study how an advertiser maximizes total conversion (e.g. ad clicks) while satisfying aggregate return-on-investment (ROI) and budget constraints across all channels. In practice, an advertiser does not have control over, and thus cannot globally optimize, which individual ad auctions she participates in for each channel, and instead authorizes a channel to procure impressions on her behalf: the advertiser can only utilize two levers on each channel, namely setting a per-channel budget and per-channel target ROI. In this work, we first analyze the effectiveness of each of these levers for solving the advertiser's global multi-channel problem. We show that when an advertiser only optimizes over per-channel ROIs, her total conversion can be arbitrarily worse than what she could have obtained in the global problem. Further, we show that the advertiser can achieve the global optimal conversion when she only optimizes over per-channel budgets. In light of this finding, under a bandit feedback setting that mimics real-world scenarios where advertisers have limited information on ad auctions in each channels and how channels procure ads, we present an efficient learning algorithm that produces per-channel budgets whose resulting conversion approximates that of the global optimal problem. Finally, we argue that all our results hold for both single-item and multi-item auctions from which channels procure impressions on advertisers' behalf.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
