Ad Blocking Whitelist Prediction for Online Publishers

Document Type

Conference Proceeding

Publication Date

12-1-2019

Abstract

The fast increase in ad blocker usage results in large revenue loss for online publishers and advertisers. Many publishers initialize counter-ad-blocking strategies, where a user has to choose either whitelisting the publisher's web site in their ad blocker or leaving the site without accessing the content. This paper aims to predict the user whitelisting behavior, which can help online publishers to better assess users' interests and design corresponding strategies. We present several techniques for personalized whitelist prediction for a target user and a target web page. Our prediction models are evaluated on real-world data provided by a large online publisher, Forbes Media. The best prediction performance was achieved using the gradient boosting regression tree model, which also demonstrated robustness and efficiency.

Identifier

85081310029 (Scopus)

ISBN

[9781728108582]

Publication Title

Proceedings 2019 IEEE International Conference on Big Data Big Data 2019

External Full Text Location

https://doi.org/10.1109/BigData47090.2019.9006402

First Page

1711

Last Page

1716

Grant

DGE 1565478

Fund Ref

National Science Foundation

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