Calibrated Viewability Prediction for Premium Inventory Expansion.

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.