Calibrated Viewability Prediction for Premium Inventory Expansion

Jonathan Schler, Allon Hammer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Billions of ads are displayed on a daily basis, making it a multi-billion industry. Most of web pages contain multiple ads, which are largely served in real time using a bidding process where buyers (advertisers) offer a price tag to the seller (publishers) for each given possible ad on the page. There are multiple factors that impact an ad price, one of the primary ones is the ad-location’s viewability likelihood. Due to the length of many web pages, certain ad locations are invisible to the visiting user, as he may not scroll far enough on the page to where the ads are placed. According to recent industry metrics, less than 60% of ads are viewable. This poses a challenge to both: buyers and sellers. Buyers want to optimize the likelihood they buy an ad that will be viewed, while sellers want to maximize ad prices (by setting higher floor prices) by providing as many possible ad placements with high viewability probability. This paper addresses the viewability prediction from the publisher’s side, and proposes a novel algorithm based on cascading gradient boosting. The algorithm enables sellers to predict an accurate viewability probability for ad impressions, which is optimized to match the actual viewability rate that will be measured for the served ads. Unlike other algorithms that optimize these problems to an average minimal difference from a central mean error, we propose an algorithm that increases the amount of extreme cases - which are the most valuable ones, thus expanding the premium ad inventory. We evaluate the algorithm on two datasets with a total of over 500 million impressions. We found that the algorithm outperforms other viewability prediction algorithms, works well for publishers while providing a measurable fairness metric to advertisers.

Original languageEnglish
Title of host publicationInformation Management and Big Data - 7th Annual International Conference, SIMBig 2020, Proceedings
EditorsJuan Antonio Lossio-Ventura, Jorge Carlos Valverde-Rebaza, Eduardo Díaz, Hugo Alatrista-Salas
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Print)9783030762278
StatePublished - 2021
Externally publishedYes
Event7th Annual International Conference on Information Management and Big Data, SIMBig 2020 - Virtual, Online
Duration: 1 Oct 20203 Oct 2020

Publication series

NameCommunications in Computer and Information Science
Volume1410 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference7th Annual International Conference on Information Management and Big Data, SIMBig 2020
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


  • Advertising technology
  • Cascading gradient boosting
  • Machine learning
  • Viewability prediction


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