Real-time bidding is the new paradigm of programmatic advertising. An
advertiser wants to make the intelligent choice of utilizing a
\textbf{Demand-Side Platform} to improve the performance of their ad campaigns.
Existing approaches are struggling to provide a satisfactory solution for
bidding optimization due to stochastic bidding behavior. In this paper, we
proposed a multi-agent reinforcement learning architecture for RTB with
functional optimization. We designed four agents bidding environment: three
Lagrange-multiplier based functional optimization agents and one baseline agent
(without any attribute of functional optimization) First, numerous attributes
have been assigned to each agent, including biased or unbiased win probability,
Lagrange multiplier, and click-through rate. In order to evaluate the proposed
RTB strategy's performance, we demonstrate the results on ten sequential
simulated auction campaigns. The results show that agents with functional
actions and rewards had the most significant average winning rate and winning
surplus, given biased and unbiased winning information respectively. The
experimental evaluations show that our approach significantly improve the
campaign's efficacy and profitability.

本文提出了一种多智能体强化学习架构以用于实时竞价广告，使用三个 Lagrange 乘数基于功能优化以及一个基准代理程序进行竞标环境设计。实验结果表明，相比其他代理程序，具有功能优化的代理程序在广告竞标过程中获胜速率和盈余率上有显著提升，表现出优异的表现和盈利性。

实时竞价的功能优化强化学习

Functional Optimization Reinforcement Learning for Real-Time Bidding

Programmatic advertising consists in automated auctioning of digital ad
space. Every time a user requests a web page, placeholders on the page are
populated with ads from the highest-bidding advertisers. The bids are typically
based on information about the user, and to an increasing extent, on
information about the surrounding media context. The growing interest in
contextual advertising is in part a counterreaction to the current dependency
on personal data, which is problematic from legal and ethical standpoints. The
transition is further accelerated by developments in Artificial Intelligence
(AI), which allow for a deeper semantic understanding of context and, by
extension, more effective ad placement. In this article, we begin by
identifying context factors that have been shown in previous research to
positively influence how ads are received. We then continue to discuss
applications of AI in contextual advertising, where it adds value by, e.g.,
extracting high-level information about media context and optimising bidding
strategies. However, left unchecked, these new practices can lead to unfair ad
delivery and manipulative use of context. We summarize these and other concerns
for consumers, publishers and advertisers in an implication analysis.

研究了程序化广告和语境广告并结合人工智能技术的应用，探讨了它们在广告投放和广告竞价策略优化方面的影响，同时指出了相关的合规和伦理问题。

基于 AI 的上下文广告：技术报告和影响分析

AI-Driven Contextual Advertising: A Technology Report and Implication Analysis

In programmatic advertising, ad slots are usually sold using second-price
(SP) auctions in real-time. The highest bidding advertiser wins but pays only
the second-highest bid (known as the winning price). In SP, for a single item,
the dominant strategy of each bidder is to bid the true value from the bidder's
perspective. However, in a practical setting, with budget constraints, bidding
the true value is a sub-optimal strategy. Hence, to devise an optimal bidding
strategy, it is of utmost importance to learn the winning price distribution
accurately. Moreover, a demand-side platform (DSP), which bids on behalf of
advertisers, observes the winning price if it wins the auction. For losing
auctions, DSPs can only treat its bidding price as the lower bound for the
unknown winning price. In literature, typically censored regression is used to
model such partially observed data. A common assumption in censored regression
is that the winning price is drawn from a fixed variance (homoscedastic)
uni-modal distribution (most often Gaussian). However, in reality, these
assumptions are often violated. We relax these assumptions and propose a
heteroscedastic fully parametric censored regression approach, as well as a
mixture density censored network. Our approach not only generalizes censored
regression but also provides flexibility to model arbitrarily distributed
real-world data. Experimental evaluation on the publicly available dataset for
winning price estimation demonstrates the effectiveness of our method.
Furthermore, we evaluate our algorithm on one of the largest demand-side
platforms and significant improvement has been achieved in comparison with the
baseline solutions.

本文提出了一个基于未完全观测数据的 heteroscedastic fully parametric censored regression 方法和混合密度 censored 网络，用于估计广告拍卖中的赢价分布，并在实验上证明了该方法的有效性。

实时竞价中可扩展的竞价预测

Scalable Bid Landscape Forecasting in Real-time Bidding

Predicting click and conversion probabilities when bidding on ad exchanges is
at the core of the programmatic advertising industry. Two separated lines of
previous works respectively address i) the prediction of user conversion
probability and ii) the attribution of these conversions to advertising events
(such as clicks) after the fact. We argue that attribution modeling improves
the efficiency of the bidding policy in the context of performance advertising.
Firstly we explain the inefficiency of the standard bidding policy with respect
to attribution. Secondly we learn and utilize an attribution model in the
bidder itself and show how it modifies the average bid after a click. Finally
we produce evidence of the effectiveness of the proposed method on both offline
and online experiments with data spanning several weeks of real traffic from
Criteo, a leader in performance advertising.

本文旨在提高推广广告招标策略的效率，并介绍了一种基于归因模型的招标方法，研究结果表明该方法有效性高且应用范围广。