1. FRM: Modeling Sponsored Search Log with Full Relational Model

2. Application Scenario

3. Why to use Click Models Target The simple case
CTR statistics of a query-ad pair in different positions
The simple case
Only one ad in a session (like tossing a coin)
Click event follows binomial distribution with a beta prior
General case: the above method cannot be utilized directly
More ads are shown together in a session
Position-bias
More factors: influence among ads, users’ intent, etc.

4. Challenge of Click Models
Examination Hypothesis
The user must examine an ad before clicking.
Problem
How to calculate p(E)?
How to estimate r?

5. Competitive Click Models
Influence among ads is not considered

6. The Influence Among the Ads
The green arrow: competing influence
The red arrow: collaborating influence

7. Data Support

8. Limitation of Previous Work
TCM model
Only modeling two ads
Only consider competing influence

9. Our Contributions
Extend TCM from modeling two ads to arbitrary number of ads
Identify the collaborating influence and incorporate it into click models
Incorporating features to further enhance click models

10. Extended TCM

11. Estimation of Parameters
Estimation of r
Estimation of lambda

12. Full Relational Model

13. Incorporating Features
Classical regression model
Prediction = f (Observation)
The model is trained in sessions containing only one ad
Incorporate the prediction into priors
Beta (alpha, beta)

14. Experimental Results
Evaluation Metrics
ROC

15. The End
Thank you very much.