This paper proposes a learning model of online ad auctions that allows for
the following four key realistic characteristics of contemporary online
auctions: (1) ad slots can have different values and click-through rates
depending on users' search queries, (2) the number and identity of competing
advertisers are unobserved and change with each auction, (3) advertisers only
receive partial, aggregated feedback, and (4) payment rules are only partially
specified. We model advertisers as agents governed by an adversarial bandit
algorithm, independent of auction mechanism intricacies. Our objective is to
simulate the behavior of advertisers for counterfactual analysis, prediction,
and inference purposes. Our findings reveal that, in such richer environments,
"soft floors" can enhance key performance metrics even when bidders are drawn
from the same population. We further demonstrate how to infer advertiser value
distributions from observed bids, thereby affirming the practical efficacy of
our approach even in a more realistic auction setting.

在线广告拍卖的学习模型揭示了竞价人行为对关键绩效指标的影响，包括广告槽位置、竞争广告商数量和身份、广告商接收的反馈以及支付规则等特征。同时证明了在更现实的拍卖环境中，即使竞标人来自同一人群，提供 “软底价” 也能提升关键绩效指标。

推进广告拍卖逼真度：实践见解和建模影响

Advancing Ad Auction Realism: Practical Insights & Modeling Implications

Advertisers play an essential role in many e-commerce platforms like Taobao
and Amazon. Fulfilling their marketing needs and supporting their business
growth is critical to the long-term prosperity of platform economies. However,
compared with extensive studies on user modeling such as click-through rate
predictions, much less attention has been drawn to advertisers, especially in
terms of understanding their diverse demands and performance. Different from
user modeling, advertiser modeling generally involves many kinds of tasks (e.g.
predictions of advertisers' expenditure, active-rate, or total impressions of
promoted products). In addition, major e-commerce platforms often provide
multiple marketing scenarios (e.g. Sponsored Search, Display Ads, Live
Streaming Ads) while advertisers' behavior tend to be dispersed among many of
them. This raises the necessity of multi-task and multi-scenario consideration
in comprehensive advertiser modeling, which faces the following challenges:
First, one model per scenario or per task simply doesn't scale; Second, it is
particularly hard to model new or minor scenarios with limited data samples;
Third, inter-scenario correlations are complicated, and may vary given
different tasks. To tackle these challenges, we propose a multi-scenario
multi-task meta learning approach (M2M) which simultaneously predicts multiple
tasks in multiple advertising scenarios.

该论文提出了一种名为 M2M 的多场景多任务元学习方法来预测广告主在不同电商平台的多种市场营销场景中的支出、活跃率、推广产品的总印象等多种任务，以解决广告主行为分散、多任务和多场景建模的挑战。

不落后：广告商建模的多场景多任务元学习方法

Leaving No One Behind: A Multi-Scenario Multi-Task Meta Learning  Approach for Advertiser Modeling

Nearly fifteen years ago, Google unveiled the generalized second price (GSP)
auction. By all theoretical accounts including their own [Varian 14], this was
the wrong auction --- the Vickrey-Clarke-Groves (VCG) auction would have been
the proper choice --- yet GSP has succeeded spectacularly.
We give a deep justification for GSP's success: advertisers' preferences map
to a model we call value maximization, they do not maximize profit as the
standard theory would believe. For value maximizers, GSP is the truthful
auction [Aggarwal 09]. Moreover, this implies an axiomatization of GSP --- it
is an auction whose prices are truthful for value maximizers --- that can be
applied much more broadly than the simple model for which GSP was originally
designed. In particular, applying it to arbitrary single-parameter domains
recovers the folklore definition of GSP. Through the lens of value
maximization, GSP metamorphosizes into a powerful auction, sound in its
principles and elegant in its simplicity.

GSP auction succeeded due to advertisers' value maximization preferences for truthful auction pricing, supporting the broader application of truthful auction pricing to single-parameter pricing domains.