A Search-based Method forForecasting 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 ImpressionVolume Critical in planning and budgeting adv

ertising campaigns Common questions for advertisers an

d 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 AdRetrieval Ad retrieval: given a page and a set of ads find the

best ads Forecasting: given an ad and a set of past impressi

ons, 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 gi

ven 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 lowe

st 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 co

unt 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: minSc

orep 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 reven

ue 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

Two-level process (Impression forecasting)

First phase (approximate) evaluation: maxWeightf = max{wf,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 minScorep 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 fa

ctor 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.