Probabilistic Models for Ad Viewability Prediction on the Web

Document Type

Article

Publication Date

9-1-2017

Abstract

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.

Identifier

85029389063 (Scopus)

Publication Title

IEEE Transactions on Knowledge and Data Engineering

External Full Text Location

https://doi.org/10.1109/TKDE.2017.2705688

ISSN

10414347

First Page

2012

Last Page

2025

Issue

9

Volume

29

Fund Ref

National Science Foundation

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