TY - JOUR
T1 - Probabilistic Models for Ad Viewability Prediction on the Web
AU - Wang, Chong
AU - Kalra, Achir
AU - Zhou, Li
AU - Borcea, Cristian
AU - Chen, Yi
N1 - Funding Information:
This research was supported by the US National Science Foundation (NSF) under Grants No. CNS 1409523, DGE 1565478, and CAREER Award IIS-1322406, as well as a Google Research Award, and an endowment from the Leir Charitable Foundations. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of US National Science Foundation.
Publisher Copyright:
© 1989-2012 IEEE.
PY - 2017/9/1
Y1 - 2017/9/1
N2 - Online display advertising has becomes a billion-dollar industry, and it keeps growing. Advertisers attempt to send marketing messages to attract potential customers via graphic banner ads on publishers' webpages. Advertisers are charged for each view of a page that delivers their display ads. However, recent studies have discovered that more than half of the ads are never shown on users' screens due to insufficient scrolling. Thus, advertisers waste a great amount of money on these ads that do not bring any return on investment. Given this situation, the Interactive Advertising Bureau calls for a shift toward charging by viewable impression, i.e., charge for ads that are viewed by users. With this new pricing model, it is helpful to predict the viewability of an ad. This paper proposes two probabilistic latent class models (PLC) that predict the viewability of any given scroll depth for a user-page pair. Using a real-life dataset from a large publisher, the experiments demonstrate that our models outperform comparison systems.
AB - Online display advertising has becomes a billion-dollar industry, and it keeps growing. Advertisers attempt to send marketing messages to attract potential customers via graphic banner ads on publishers' webpages. Advertisers are charged for each view of a page that delivers their display ads. However, recent studies have discovered that more than half of the ads are never shown on users' screens due to insufficient scrolling. Thus, advertisers waste a great amount of money on these ads that do not bring any return on investment. Given this situation, the Interactive Advertising Bureau calls for a shift toward charging by viewable impression, i.e., charge for ads that are viewed by users. With this new pricing model, it is helpful to predict the viewability of an ad. This paper proposes two probabilistic latent class models (PLC) that predict the viewability of any given scroll depth for a user-page pair. Using a real-life dataset from a large publisher, the experiments demonstrate that our models outperform comparison systems.
KW - Computational advertising
KW - user behavior
KW - viewability prediction
UR - http://www.scopus.com/inward/record.url?scp=85029389063&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029389063&partnerID=8YFLogxK
U2 - 10.1109/TKDE.2017.2705688
DO - 10.1109/TKDE.2017.2705688
M3 - Article
AN - SCOPUS:85029389063
SN - 1041-4347
VL - 29
SP - 2012
EP - 2025
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 9
M1 - 7931647
ER -