Please read Dr. Chen’s article from the IEEE International Conference on Big Data titled, Ad Blocking Whitelist Prediction for Online Publishers. 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. To read the full article.
- How can NJ cut high death rate among Black mothers in childbirth? This group has a plan
- Could polio be making comeback in Delaware Valley?
- Soaring Overdose Rates in the Pandemic Reflected Widening Racial Disparities
- Rutgers Will Conduct the Most Comprehensive Study Ever Done of High-Risk Children Newly Diagnosed With Type 1 Diabetes.
- A U.S.-Russia nuclear war could starve 5 billion to death, study says.