@inproceedings{98d56a0c5cc148f487035b7e53d130ad,
title = "Ad Blocking Whitelist Prediction for Online Publishers",
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.",
keywords = "ad blocking, gradient boosting regression tree, online advertising, user behavior prediction",
author = "Shuai Zhao and Achir Kalra and Chong Wang and Cristian Borcea and Yi Chen",
year = "2019",
month = dec,
doi = "10.1109/BigData47090.2019.9006402",
language = "English (US)",
series = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1711--1716",
editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Hu, {Xiaohua Tony} and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, {Yanfang Fanny}",
booktitle = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",
address = "United States",
note = "2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",
}