The document discusses real-time bidding (RTB) and its current landscape, including predictive modeling techniques like logistic regression and decision trees. It highlights the importance of data processing, variable transformation, and revenue maximization strategies, especially in the context of fraud detection. The document also emphasizes collaboration between statisticians and engineers to address modeling challenges and enhance performance.

1. RTB 优化算法
Real Time Bidding

2. 第15期：
《Technology Changes 》-悠易互通专场
www.LAMPER.cn
http://weibo.com/lampercn

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. 什么是Real Time Bidding
Ad selection via bidding
上图引用自Stanford MS&E 239

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. 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. 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. 谢 谢！