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In the Mood to Click? Towards Inferring Receptiveness to Search Advertising

In the Mood to Click? Towards Inferring Receptiveness to Search Advertising. WI 2009. Outline. Introduction Define Contextualized Interaction model Modeling and classifier implementation Experiment Conclusion and future work. Introduction. Goal: Select the “right time” to serve an ad.

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In the Mood to Click? Towards Inferring Receptiveness to Search Advertising

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  1. In the Mood to Click? Towards Inferring Receptiveness to Search Advertising WI 2009

  2. Outline • Introduction • Define • Contextualized Interaction model • Modeling and classifier implementation • Experiment • Conclusion and future work

  3. Introduction • Goal: Select the “right time” to serve an ad. • Interaction feature: ex: mouse movements, clicks, page scrolls.

  4. Define(1/3) • Search session: a sequence which is no time gap of greater than 30 minutes between any two consecutive searches. • Search mission: (within a session) a query of each search shares at least one non-stopword query term with at least one previous (but not necessarily consecutive) query.

  5. Define(2/3)

  6. Define(3/3) • S(s1,…,si,…,sm): search mission • Predicting Advertising Receptiveness: predict whether the searcher will click on an ad presented on the results for any of the future searches si+1,si+2,…,sm within the current search mission S. • Reduce to searcher receptiveness: clickthrough of search engines is low (google CTR in dataset is about 1%)

  7. CxI system CxI(Contextualized Interaction model) • Data collection: use LibX toolbar

  8. CxI - Features(1/2) • Context features: query, clicked URL, page content, number of ads… • Interaction features: time before first move, dwell time, the ways of entering the page(issue a new query or “back” button) • Global features: length, vertical and horizontal ranges of trajectories

  9. CxI - Features(2/2) • Trajectory features: speed, acceleration, rotation of mouse movement • Hovering features: hovering over the ad regions on pages(boolean features) • Ad clickthrough features: ad clickthrough on current page

  10. Two modeling approaches • Page-Level(stateless) Model: predict for each query, then combine predictions to classify the mission as receptive or not • Session-Level contextual Model: infer the most likely sequence of hidden states and predict future ad clickthrough

  11. Classifier implementation • Page-level classification: use Support Vector Machines • Session-level classification: use CRF(Condictional Random Fields)

  12. CRF of Session-Level Model • R: Receptive/Expect Ad Click in future search in the mission • N: Do Not Expect Ad Click in future search

  13. Experiment(1/5) • Dataset: • The data was gathered from 8/15 through 12/15 2008 • 440 users who have clicked on a search ad at least once • 6576 search sessions, 17123 search missions and 45212 searches

  14. Experiment(2/5) • Metrics • Precision(P): fraction of True Positive over all predicted positives in the mission • Recall(R): fraction of correct positive predictions(TP) over all positive labels in the mission • F1-measure(F1): 2*P*R / (P+R)

  15. Experiment(3/5) Methods compared • QC(Query Chains):using query strings, page URLs… • QCLK: clicked URL • PC(Page-level Context) • CxI(C): using context features • CxI(C+I): using context features and interation features

  16. Experiment(4/5) • 80% of the sessions for training, 20% for testing(5-fold Cross Validation)

  17. Experiment(5/5) • Copy positive training samples five times • Assign higher weight to positive class Substantial improvement on P and F1

  18. Conclusion And Future Work • Present new model for predicting ad clickthrough on search ads • Hypothesize the existence of “hidden state” which corresponds to ad receptiveness of user • Add personal user history

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