A Search-based Method for Forecasting Ad Impression in Contextual Advertising

1. A Search-based Method forForecasting Ad Impression in Contextual Advertising Defense

2. Overview • Background: Web and contextual advertising • Motivation: importance of volume forecasting in contextual advertising • Methodology: forecasting volume as an inverse of the ad retrieval • Experiments

3. Web Advertising • Huge impact on the Web and beyond • $21 billion industry • Main textual advertising channels: • Search advertising • Contextual advertising

4. Contextual Advertising (CA)

5. CA Basics • Supports a variety of the web ecosystem • Selects ads based on the “context”: • Web page where the ads are placed • Users that are viewing this page • Interplay of three participants: • Publisher • Advertiser • Ad network • Advertiser’s goal is to obtain web traffic

6. Importance of ImpressionVolume • Critical in planning and budgeting advertising campaigns • Common questions for advertisers and intermediaries: • Bid value • Impact of ad variations • Timing of the campaign

7. A Challenging Problem of Impression forecasting • CA platforms are complex systems • Have hundreds of contributing features • A moving target, dynamic • Publisher‘s content and traffic vary over time • Large scale computation: billions of page views, hundreds of millions of distinct pages, and hundreds of millions of ads • Dynamic bid landscape • Competitors and what they are willing to pay

8. Current practice • Run test ad in real traffic for a few days • Simultaneously with the baseline • Compare with the baseline • Obvious drawbacks: • Use ad serving infrastructure • Expensive • Inefficient • Very long turn-around time

9. Forecasting as Inverse of AdRetrieval • Ad retrieval: given a page and a set of ads find the best ads • Forecasting: given an ad and a set of past impressions, find where the ad would have been shown if it were in the system • This work: assumes ads selected based on similarity of features: • Use the WAND (Broder et al, CIKM 2003) DAAT algorithm as page selection • Similarity of ad and context feature vectors: requires monotonic scoring function – this work uses dot product • Features can be based on either user of page context.

10. Conceptual Work Flow • Keep all the data used in ad retrieval for a given period • For an unseen/incoming ad: • Examine each impression • Score the ad using the ad retrieval algorithm • Compare the ad score with the score of the lowest ranking ad shown in the page view • Count the impressions where the ad would have been shown

11. Main challenge: scale • In order to beat scalability problem: • Index only unique pages • Adaptation of the WAND algorithm for count aggregation needed in forecasting • A Two-level Process • Use a posting list order to allow early termination

12. Indexing Unique Pages • The revenue estimate of an ad-page pair: score(p,a) = similarity(p,a)*bid • Revenue estimate for the lowest ranking ad: minScorep • For repeating pages the similarity is constant • However, ads and bids vary: • Could change the lowest ranking ad of a unique page • Only one index entry per unique page: What revenue to store for the lowest ranking ads? • Save a distribution of estimates {rev1…revn} • Assign median to the minScorep • MinScorep is recomputed based on the current ad supply

13. Two-level process (Impression forecasting) • First phase (approximate) evaluation: • maxWeightf = max{wf,p : for all p} • Full evaluation:

14. Framework • Offline processing • Analyzing the pages • Building a page inverted index • Creating a page statistics file • Online processing • We use the inverted page index and page statistics to forecast the # of impressions of a given ad. • Output • Given a ad and bid, output the # of imp • Give a ad, output the curve describe the relation b/w bid and # of impressions

15. Experiment Results • Day to day forecast • Week to week forecast

16. Observations: • Similar results between day-day and week-week forecasting. • The errors seems big, however, • Due to the traffic fluctuation. • Even with large margin of error, our result is still significant (it’s the best of its kind, and it’s still acceptable in campaigning budgeting and advertising strategy)

17. Top row has a good prediction. • Bottom row does not match well due to traffic fluctuation, but match the trend and sharp very well.

18. Tradeoff b/w efficiency and accuracy • Changing the value of minScorep will have effect on the output of the first level

19. Ad Variation Example • Subtle difference could lead to dramatic performance change

20. Conclusion • Ad retrieval algorithm is the determining factor in the CA impression volume forecasting • Introduced a search-based forecasting as inverse of ad retrieval • Promising experimental results • Further work: combine search with learning approaches to further improve forecasting.