论文标题
AutOFR：自动过滤器规则生成用于adblocking
AutoFR: Automated Filter Rule Generation for Adblocking
论文作者
论文摘要
Adblocking取决于过滤器列表，这些列表由过滤器列表作者社区手动策划和维护。过滤列表策划是一个费力的过程，它不能很好地扩展到大量站点或随着时间的流逝。在本文中，我们介绍了AUTOFR，这是一个加强学习框架，以完全自动化过滤规则创建和评估感兴趣的站点的过程。我们设计了一种基于多臂匪徒的算法，以生成过滤器规则，该滤波器在控制阻断广告和避免视觉破裂之间的权衡时会阻止广告。我们在数千个站点上测试AUTOFR，并表明它是有效的：为感兴趣的站点生成过滤器规则只需要几分钟即可。 AUTOFR是有效的：它生成的过滤器规则可以阻止AD的86％，而easyList则可以实现可比较的视觉断裂。此外，AUTOFR生成的过滤器规则可以很好地推广到新站点。我们设想，自动公司可以大规模协助隔离社区的过滤器规则生成。
Adblocking relies on filter lists, which are manually curated and maintained by a community of filter list authors. Filter list curation is a laborious process that does not scale well to a large number of sites or over time. In this paper, we introduce AutoFR, a reinforcement learning framework to fully automate the process of filter rule creation and evaluation for sites of interest. We design an algorithm based on multi-arm bandits to generate filter rules that block ads while controlling the trade-off between blocking ads and avoiding visual breakage. We test AutoFR on thousands of sites and we show that it is efficient: it takes only a few minutes to generate filter rules for a site of interest. AutoFR is effective: it generates filter rules that can block 86% of the ads, as compared to 87% by EasyList, while achieving comparable visual breakage. Furthermore, AutoFR generates filter rules that generalize well to new sites. We envision that AutoFR can assist the adblocking community in filter rule generation at scale.
