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.
- Exposure to Gun Violence Is Associated With Suicidal Behavior in Black Adults.
- Join NJ ACTS Special Populations Core Seminar Series on March 27 at 12pm
- Rutgers Health and RWJBarnabas Health Receive Grant to Train Health Professionals and Improve Care.
- NJACTS Community Engagement Core Available Services
- Injected antipsychotics may be more effective against schizophrenia than pills.