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<a href="/archives/2021" class="archive-year"><i class="icon fa fa-calendar-o"></i>2021</a>
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<article class="article article-summary">
<div class="article-summary-inner">
<a href="/2021/11/14/paper/rtb/Deep%20Landscape%20Forecasting%20for%20Real-Time%20Bidding/" class="thumbnail">
<span style="background-image:url(/images/paper/rtb_deep_forecast_1.png)" alt="Deep Lanscape Forecasting for Real-Time Bidding" class="thumbnail-image"></span>
<span class="comment-counter">
<i class="fa fa-comments-o"></i>
<span class="disqus-comment-count" data-disqus-identifier="" data-disqus-url="http://csxiang18.github.io/2021/11/14/paper/rtb/Deep%20Landscape%20Forecasting%20for%20Real-Time%20Bidding/">0</span>
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<a class="article-category-link" href="/categories/%E8%AE%BA%E6%96%87/">论文</a>
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<div class="date"><time datetime="2021-11-14T04:00:00.000Z" itemprop="datePublished">2021-11-14</time></div>
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<h1 class="article-title" itemprop="name">
<a href="/2021/11/14/paper/rtb/Deep%20Landscape%20Forecasting%20for%20Real-Time%20Bidding/">Deep Lanscape Forecasting for Real-Time Bidding</a>
</h1>
<p class="article-excerpt">
本文解决的问题是为advertiser预测一个一个竞价流量的价值\(z\)，在二价的场景下面称作market price。这个问题主要有两个难点。
对于在RTB市场fail的bid，只能知道自己的出价\(b\)低于market price \(z\)，这类被称作censored data。
通常会假设\(z\)是服从于某个假定的分布，但在实际应用中通过不了类似的假设检验
论文作者使用了两种方式
</p>
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</article>
<article class="article article-summary">
<div class="article-summary-inner">
<a href="/2021/10/29/paper/rtb/Feedback%20Control%20of%20Real-Time%20Display%20Advertising/" class="thumbnail">
<span style="background-image:url(/images/paper/feedback_1.png)" alt="Feedback Control of Real-Time Display Advertising" class="thumbnail-image"></span>
<span class="comment-counter">
<i class="fa fa-comments-o"></i>
<span class="disqus-comment-count" data-disqus-identifier="" data-disqus-url="http://csxiang18.github.io/2021/10/29/paper/rtb/Feedback%20Control%20of%20Real-Time%20Display%20Advertising/">0</span>
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<a class="article-category-link" href="/categories/%E8%AE%BA%E6%96%87/">论文</a>
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<div class="date"><time datetime="2021-10-28T16:00:00.000Z" itemprop="datePublished">2021-10-29</time></div>
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<h1 class="article-title" itemprop="name">
<a href="/2021/10/29/paper/rtb/Feedback%20Control%20of%20Real-Time%20Display%20Advertising/">Feedback Control of Real-Time Display Advertising</a>
</h1>
<p class="article-excerpt">
一篇基于反馈控制系统来控制DSP的竞价策略。解决的问题是需要在一定的budget约束条件下，通过调整我的竞价，达到控制广告主的一些KPI的目的，比如本文里面的eCPC，AWR或者CTR等。
最早的基于request的竞价策略为 \[
b(t) = b_0 \frac{\theta_t}{\theta_0} \qquad (1)
\] \(\theta_t\)是当前的utility，比如CTR, e
</p>
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</article>
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<div class="article-row">
<article class="article article-summary">
<div class="article-summary-inner">
<a href="/2021/06/02/paper/rtb/Budget%20Constrained%20Bidding%20by%20Model-free%20Reinforcement%20Learning%20in%20Display%20Advertising/" class="thumbnail">
<span style="background-image:url(/images/paper/rtb_rl_2.png)" alt="Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising" class="thumbnail-image"></span>
<span class="comment-counter">
<i class="fa fa-comments-o"></i>
<span class="disqus-comment-count" data-disqus-identifier="" data-disqus-url="http://csxiang18.github.io/2021/06/02/paper/rtb/Budget%20Constrained%20Bidding%20by%20Model-free%20Reinforcement%20Learning%20in%20Display%20Advertising/">0</span>
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</a>
<div class="article-meta">
<div class="category">
<a class="article-category-link" href="/categories/%E8%AE%BA%E6%96%87/">论文</a>
</div>
<div class="date"><time datetime="2021-06-01T16:00:00.000Z" itemprop="datePublished">2021-06-02</time></div>
</div>
<h1 class="article-title" itemprop="name">
<a href="/2021/06/02/paper/rtb/Budget%20Constrained%20Bidding%20by%20Model-free%20Reinforcement%20Learning%20in%20Display%20Advertising/">Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising</a>
</h1>
<p class="article-excerpt">
DSP/广告主再RTB采取的出价策略通常是在一定的budget限制条件下，得到最优的价值。所以可以形式化表示为 \[
\max \sum_{i=1,\cdots,N} x_iv_i \\
s.t. \sum_{i=1}^n x_ic_i \le B
\] \(v_i\)是当前流量的价值，通常可以用CTR/CVR去衡量。在上一篇RTB的出价策略中，见竞价策略一。我们可以知道，在二价的场景下，最优
</p>
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<div class="article-summary-inner">
<a href="/2021/04/19/paper/rtb/Real-Time%20Bidding%20by%20Reinforcement%20Learning%20in%20Display%20Advertising/" class="thumbnail">
<span style="background-image:url(/images/paper/rtb_rl_1.png)" alt="Real-Time Bidding by Reinforcement Learning in Display Advertising" class="thumbnail-image"></span>
<span class="comment-counter">
<i class="fa fa-comments-o"></i>
<span class="disqus-comment-count" data-disqus-identifier="" data-disqus-url="http://csxiang18.github.io/2021/04/19/paper/rtb/Real-Time%20Bidding%20by%20Reinforcement%20Learning%20in%20Display%20Advertising/">0</span>
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<a class="article-category-link" href="/categories/%E8%AE%BA%E6%96%87/">论文</a>
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<div class="date"><time datetime="2021-04-18T16:00:00.000Z" itemprop="datePublished">2021-04-19</time></div>
</div>
<h1 class="article-title" itemprop="name">
<a href="/2021/04/19/paper/rtb/Real-Time%20Bidding%20by%20Reinforcement%20Learning%20in%20Display%20Advertising/">Real-Time Bidding by Reinforcement Learning in Display Advertising</a>
</h1>
<p class="article-excerpt">
Real-Time Bidding by Reinforcement Learning in Display Advertising
很早的一篇用RL来解决advertiser竞价的问题。把advertiser的竞价行为看成是一轮一轮的游戏。在每一轮游戏过程中总共有T次竞价，advertiser的budget的上限为B，每次竞价中opportunity的特征为\(x\).
首先对state的定义为
</p>
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<article class="article article-summary">
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<a href="/2021/04/11/paper/rtb/Optimal%20Real%20Time%20Bidding%20for%20Display%20Advertising/" class="thumbnail">
<span style="background-image:url(/images/paper/bid_flow_1.png)" alt="Optimal Real Time Bidding for Display Advertising" class="thumbnail-image"></span>
<span class="comment-counter">
<i class="fa fa-comments-o"></i>
<span class="disqus-comment-count" data-disqus-identifier="" data-disqus-url="http://csxiang18.github.io/2021/04/11/paper/rtb/Optimal%20Real%20Time%20Bidding%20for%20Display%20Advertising/">0</span>
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<a class="article-category-link" href="/categories/%E8%AE%BA%E6%96%87/">论文</a>
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<div class="date"><time datetime="2021-04-10T16:00:00.000Z" itemprop="datePublished">2021-04-11</time></div>
</div>
<h1 class="article-title" itemprop="name">
<a href="/2021/04/11/paper/rtb/Optimal%20Real%20Time%20Bidding%20for%20Display%20Advertising/">Optimal Real Time Bidding for Display Advertising</a>
</h1>
<p class="article-excerpt">
Optimal Real Time Bidding for Display Advertising
主要解决了在有budget constrait的约束条件下，DSP如何出价去maximize自己的KPI (CTR, CVR,...)
很经典的一个框架，解决的问题是为DSP设计一个竞价机制，要满足
1. budget约束
2. 最大化广告主的KPI
整体DSP的竞价过程如下图所示：
假设有N个r
</p>
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</article>
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<a href="/archives/2019" class="archive-year"><i class="icon fa fa-calendar-o"></i>2019</a>
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<article class="article article-summary">
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<a href="/2019/09/13/travel/Haisenwai/" class="thumbnail">
<span style="background-image:url(/images/travel/haishenwai/haishenwai_plan.png)" alt="海参崴之行" class="thumbnail-image"></span>
<span class="comment-counter">
<i class="fa fa-comments-o"></i>
<span class="disqus-comment-count" data-disqus-identifier="" data-disqus-url="http://csxiang18.github.io/2019/09/13/travel/Haisenwai/">0</span>
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<a class="article-category-link" href="/categories/%E6%97%85%E6%B8%B8/">旅游</a>
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<div class="date"><time datetime="2019-09-12T16:00:00.000Z" itemprop="datePublished">2019-09-13</time></div>
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<h1 class="article-title" itemprop="name">
<a href="/2019/09/13/travel/Haisenwai/">海参崴之行</a>
</h1>
<p class="article-excerpt">
终于回来补上这篇游记了。这次去海参崴的时间是2019年9月13-2019年9月17日。事前经过各位靠谱的小伙伴谋划，我们的行程安排大致如下图：
行程计划
去前准备： * 俄罗斯的电话卡（淘宝） * 下载的App: Yandex（打车），Google Translate, Goole Maps * 兑好的卢布，每人1000RMB
2019-9-13
早上淡定的去赶飞机了。去程是北京到哈尔滨转机大
</p>
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