FRM: Modeling Sponsored Search Log with Full Relational Model

Application Scenario

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.

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

Competitive Click Models Influence among ads is not considered

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

Data Support

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

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

Extended TCM

Estimation of Parameters Estimation of r Estimation of lambda

Full Relational Model

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)

Experimental Results Evaluation Metrics ROC

The End Thank you very much. xxin@cse.cuhk.edu.hk