Audience validation in online media using limited behavioral data and demographic mix

At the onset of any campaign in an online media, an advertiser usually provides a specific demographic "target" to a content publisher. But limited individual profile information leads to low accuracy in targeting. A third party (like Nielsen, Comscore) validates the percentage success of "in-Target" impressions. Low accuracy in targeting is expensive as the publisher gets paid only for the impressions which were on target. However, publishers could get access to the demographic mix information for each show from the third party. In this work, we propose a new approach to incorporate user level latent features developed from the show-wise demographic mix provided by the third party, along with session level features of viewers to improve the demographic predictions. In congruence with current industry practice, we train our model on a small labeled data and establish the effectiveness of our approach over existing approaches through experiments.