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

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

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

Contextual Advertising (CA)

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

Importance of Impression Volume Critical in planning and budgeting advertising campaigns Common questions for advertisers and intermediaries: Bid value Impact of ad variations Timing of the campaign

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

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

Forecasting as Inverse of Ad Retrieval 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.

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

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

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: minScore p 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 minScore p MinScore p is recomputed based on the current ad supply

Two-level process (Impression forecasting) First phase (approximate) evaluation: maxWeight f = max{w f,p : for all p} Full evaluation:

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

Experiment Results Day to day forecast Week to week forecast

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)

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

Tradeoff b/w efficiency and accuracy Changing the value of minScore p will have effect on the output of the first level

Ad Variation Example Subtle difference could lead to dramatic performance change

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.