AutoFR: Automated Filter Rule Generation for Adblocking
AutoFR: Automated Filter Rule Generation for Adblocking
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发表时间：
2022-02
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通讯作者：
Hieu Le;Salma Elmalaki;A. Markopoulou;Zubair Shafiq
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作者：
Hieu Le;Salma Elmalaki;A. Markopoulou;Zubair Shafiq
Adblocking依赖于过滤器列表，这些列表由过滤器列表作者社区手动策划和维护。过滤器列表管理是一个费力的过程，不能很好地扩展到大量网站或随着时间的推移。在本文中，我们介绍了AutoFR，这是一个强化学习框架，可以完全自动化过滤规则的创建和评估过程。我们设计了一种基于多臂强盗的算法来生成过滤规则，该过滤规则在阻止广告的同时控制阻止广告和避免视觉破坏之间的权衡。我们在数千个网站上测试了AutoFR，并证明它是有效的：只需几分钟即可为感兴趣的网站生成过滤规则。AutoFR有效：它生成的过滤规则可以阻止86%的广告，相比之下，EasyList的比例为87%，同时实现了类似的视觉破坏。此外，AutoFR生成的过滤规则可以很好地推广到新网站。我们设想AutoFR可以帮助广告拦截社区大规模地生成过滤规则。
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.