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RTB 优化算法 Real Time Bidding
第15期:    《Technology Changes 》-悠易互通专场             www.LAMPER.cn        http://weibo.com/lampercn
Overview                            Real Time Bidding •   什么是Real Time Bidding •   RTB现况 •   数据处理 •   变量转化和选择 •   预测模型
什么是Real Time Bidding                 Ad selection via bidding                    上图引用自Stanford MS&E 239
RTB现况 Predictive Modeling: •   Logistic Regression •   Decision Tree                                             Global RT...
变量转化和选择• Bivariate Analysis          1       Bivariate analysis removing variables provided no                            ...
预测模型Objective:•   A ranking system to rank the probability of clicking or conversion from high to low.Challenges:•   More ...
谢   谢!
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RTB 优化算法

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《RTB 优化算法》
介绍:在DSP的系统里面会运用到大量统计的模型和数据挖掘的方法。直接搬运国外同类公司的方法大多会和预期相差很大。由于中国数据的不完整性和不准确性,各个公司的数据挖掘人才往往需要更多的创新和研究,才能达到理想的效果。这次我们会通过一个实际投放的例子,和大家分享在DSP系统中如何通过RTB一步步达到ROI的提升
嘉宾:卢磊

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Transcript of "RTB 优化算法"

  1. 1. RTB 优化算法 Real Time Bidding
  2. 2. 第15期: 《Technology Changes 》-悠易互通专场 www.LAMPER.cn http://weibo.com/lampercn
  3. 3. Overview Real Time Bidding • 什么是Real Time Bidding • RTB现况 • 数据处理 • 变量转化和选择 • 预测模型
  4. 4. 什么是Real Time Bidding Ad selection via bidding 上图引用自Stanford MS&E 239
  5. 5. RTB现况 Predictive Modeling: • Logistic Regression • Decision Tree Global RTB Leaders: • 2-Stage Generalized Linear • MediaMath Model Advertisers Media • Turn • Etc. • X+1 • DataXu Optimization: • Brandscreen • Dashboard Driven Manual Optimization User • Data Driven Revenue China RTB Leaders: Maximization • 悠易互通 • Revenue Maximization with Fraud Detection
  6. 6. 变量转化和选择• Bivariate Analysis 1 Bivariate analysis removing variables provided no information on model• Transformation 2 Fraud Detection and Data Trimming• Data Accuracy• Clustering 3 Clustering and Grouping 5• Numerical vs. Categorical 4 Logistic regression Scorecarding• Indexing Indexing• More Variable Creation Decision tree Final Model 6 7
  7. 7. 预测模型Objective:• A ranking system to rank the probability of clicking or conversion from high to low.Challenges:• More variables and hierarchies in the model is likely to incur bias. Statistician Engineer• Reduce variables and hierarchies reduce variance• Model not preforming well with a couple of variables only• Easy implementation. Statistician and Engineers to work as a team.
  8. 8. 谢 谢!
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