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Traffic Shaping to Optimize Ad Delivery

Traffic Shaping to Optimize Ad Delivery. Deepayan Chakrabarti Erik Vee. Traffic Shaping. Which article summary should be picked? Ans : The one with highest expected CTR. Which ad should be displayed? Ans : The ad that minimizes underdelivery. Article pool. Underdelivery.

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Traffic Shaping to Optimize Ad Delivery

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  1. Traffic Shaping to Optimize Ad Delivery DeepayanChakrabarti Erik Vee

  2. Traffic Shaping Which article summary should be picked? Ans:The one with highest expected CTR Which ad should be displayed? Ans:The ad that minimizes underdelivery Article pool

  3. Underdelivery • Advertisers are guaranteed some impressions (say, 1M) over some time (say, 2 months) • only to users matching their specs • only when they visit certain types of pages • only on certain positions on the page • An underdelivering ad is one that is likely to miss its guarantee

  4. Traffic Shaping Which article summary should be picked? Ans:The one with highest expected CTR Which ad should be displayed? Ans:The ad that minimizes underdelivery Goal: Combine the two

  5. Traffic Shaping • Goal: Bias the article summary selection to • reduce under-delivery • but insignificant drop in CTR • AND do this in real-time

  6. Outline • Formulation as an optimization problem • Real-time solution • Empirical results

  7. Formulation Ad delivery fraction φℓj ℓ j Demand dj Traffic shaping fraction wki i Supply sk CTRcki k k:(user) j:(ads) i:(user, article) ℓ:(user, article, position)“Fully Qualified Impression” Goal: Infer traffic shaping fractions wki

  8. Ad delivery fraction φℓj Formulation Traffic shaping fraction wki A CTRcki • Full traffic shaping graph: • All forecasted user traffic X all available articles • arriving at the homepage, • or directly on article page • Goal: Infer wki • But forced to infer φℓjas well B C Full Traffic Shaping Graph

  9. Outline • Formulation as an optimization problem • Real-time solution • Empirical results

  10. Formulation • Reformulation: {wki, φℓj}→ zℓj • Convex program  can be solved optimally

  11. Formulation • But we have another problem • At runtime, we must shape every incoming user without looking at the entire graph • Solution: • Periodically solve the convex problem offline • Store a cache derived from this solution • Reconstruct the optimal solution for each user at runtime, using only the cache

  12. Real-time solution Cache these Reconstruct using these All constraints can be expressed as constraints on σℓ

  13. Results • Data: • Historical traffic logs from April, 2011 • 25K user nodes • Total supply weight > 50B impressions • 100K ads

  14. Lift in impressions Nearly threefold improvement via traffic shaping Lift in impressions delivered to underperforming ads Fraction of traffic that is not shaped

  15. Average CTR CTR drop < 10% Average CTR (as percentage of maximum CTR) Fraction of traffic that is not shaped

  16. Summary • 3x underdelivery reduction with <10% CTR drop • 2.6x reduction with 4% CTR drop • Runtime application needs only a small cache

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