We propose a general stochastic framework for modelling repeated auctions in
the Real Time Bidding (RTB) ecosystem using point processes. The flexibility of
the framework allows a variety of auction scenarios including configuration of
information provided to player, determination of auction winner and
quantification of utility gained from each auctions. We propose theoretical
results on how this formulation of process can be approximated to a Poisson
point process, which enables the analyzer to take advantage of well-established
properties. Under this framework, we specify the player's optimal strategy
under various scenarios. We also emphasize that it is critical to consider the
joint distribution of utility and market condition instead of estimating the
marginal distributions independently.

我们提出了一个通用的随机框架，用于模拟实时竞价生态系统中的重复拍卖，该框架利用点过程进行建模。该框架的灵活性允许各种拍卖场景，包括为玩家提供的信息配置，确定拍卖获胜者以及量化每次拍卖所获得的效用。我们提出了有关如何将该过程的形式化近似为泊松点过程的理论结果，这使得分析人员可以利用成熟的属性。在该框架下，我们指定了在各种情景下玩家的最优策略。我们还强调了必须考虑效用和市场条件的联合分布，而不是独立估计边际分布的重要性。

利用点过程进行 RTB 建模

RTB Formulation Using Point Process

Today, billions of display ad impressions are purchased on a daily basis
through a public auction hosted by real time bidding (RTB) exchanges. A
decision has to be made for advertisers to submit a bid for each selected RTB
ad request in milliseconds. Restricted by the budget, the goal is to buy a set
of ad impressions to reach as many targeted users as possible. A desired action
(conversion), advertiser specific, includes purchasing a product, filling out a
form, signing up for emails, etc. In addition, advertisers typically prefer to
spend their budget smoothly over the time in order to reach a wider range of
audience accessible throughout a day and have a sustainable impact. However,
since the conversions occur rarely and the occurrence feedback is normally
delayed, it is very challenging to achieve both budget and performance goals at
the same time. In this paper, we present an online approach to the smooth
budget delivery while optimizing for the conversion performance. Our algorithm
tries to select high quality impressions and adjust the bid price based on the
prior performance distribution in an adaptive manner by distributing the budget
optimally across time. Our experimental results from real advertising campaigns
demonstrate the effectiveness of our proposed approach.

本研究提出了一种在线方法，通过在时间范围内最优化地分配预算，并基于事前性能分布来选择高质量的广告展示和调整竞价，以实现顺畅的预算交付并优化转化表现。