Filter Rule

Filter rules are sets of instructions used to selectively block or allow specific content, primarily in applications like ad-blocking. Current research focuses on automating the creation and refinement of these rules, employing techniques such as reinforcement learning and saliency-based detection to improve accuracy and efficiency while minimizing unintended consequences like breaking legitimate website functionality. This work aims to improve the effectiveness and scalability of content filtering systems, impacting areas such as privacy enhancement and online advertising.