In this work, we investigate the online learning problem of revenue
maximization in ad auctions, where the seller needs to learn the click-through
rates (CTRs) of each ad candidate and charge the price of the winner through a
pay-per-click manner. We focus on two models of the advertisers' strategic
behaviors. First, we assume that the advertiser is completely myopic; i.e.~in
each round, they aim to maximize their utility only for the current round. In
this setting, we develop an online mechanism based on upper-confidence bounds
that achieves a tight $O(\sqrt{T})$ regret in the worst-case and negative
regret when the values are static across all the auctions and there is a gap
between the highest expected value (i.e.~value multiplied by their CTR) and
second highest expected value ad. Next, we assume that the advertiser is
non-myopic and cares about their long term utility. This setting is much more
complex since an advertiser is incentivized to influence the mechanism by
bidding strategically in earlier rounds. In this setting, we provide an
algorithm to achieve negative regret for the static valuation setting (with a
positive gap), which is in sharp contrast with the prior work that shows
$O(T^{2/3})$ regret when the valuation is generated by adversary.

我们研究了在线学习问题中的广告拍卖收入最大化问题，重点关注广告主的战略行为、点击率和动态估值对机制表现的影响。

广告竞拍中点击率预测的改进在线学习算法

Improved Online Learning Algorithms for CTR Prediction in Ad Auctions

In many applications, ads are displayed together with the prices, so as to
provide a direct comparison among similar products or services. The
price-displaying feature not only influences the consumers' decisions, but also
affects the advertisers' bidding behaviors. In this paper, we study ad auctions
with display prices from the perspective of mechanism design, in which
advertisers are asked to submit both the costs and prices of their products. We
provide a characterization for all incentive compatible auctions with display
prices, and use it to design auctions under two scenarios. In the former
scenario, the display prices are assumed to be exogenously determined. For this
setting, we derive the welfare-maximizing and revenue-maximizing auctions for
any realization of the price profile. In the latter, advertisers are allowed to
strategize display prices in their own interests. We investigate two families
of allocation policies within the scenario and identify the equilibrium prices
accordingly. Our results reveal that the display prices do affect the design of
ad auctions and the platform can leverage such information to optimize the
performance of ad delivery.

研究了广告拍卖中的显示价格对机制设计的影响，提出了所有激励兼容的拍卖策略，并使用它来设计两种情景下的拍卖，结果表明显示价格确实影响拍卖的设计并且平台可以利用这些信息来优化广告投放的表现。

广告展示价格的机制设计

Mechanism Design for Ad Auctions with Display Prices

We examine trade-offs among stakeholders in ad auctions. Our metrics are the
revenue for the utility of the auctioneer, the number of clicks for the utility
of the users and the welfare for the utility of the advertisers. We show how to
optimize linear combinations of the stakeholder utilities, showing that these
can be tackled through a GSP auction with a per-click reserve price. We then
examine constrained optimization of stakeholder utilities.
We use simulations and analysis of real-world sponsored search auction data
to demonstrate the feasible trade-offs, examining the effect of changing the
allowed number of ads on the utilities of the stakeholders. We investigate both
short term effects, when the players do not have the time to modify their
behavior, and long term equilibrium conditions.
Finally, we examine a combinatorially richer constrained optimization
problem, where there are several possible allowed configurations (templates) of
ad formats. This model captures richer ad formats, which allow using the
available screen real estate in various ways. We show that two natural
generalizations of the GSP auction rules to this domain are poorly behaved,
resulting in not having a symmetric Nash equilibrium or having one with poor
welfare. We also provide positive results for restricted cases.

本文研究广告拍卖中各利益相关者的权衡。我们讨论了基于利益相关者效用值的线性组合优化问题及其解决方案，同时通过模拟和实际数据分析探究了不同广告数量对各方效用的影响。最后，我们探究了组合型广告格式的不同竞价规则，并发现了其中的问题和限制。