Personalized Dynamic Counter Ad-Blocking Using Deep Learning
Personalized Dynamic Counter Ad-Blocking Using Deep Learning
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DOI：
10.1109/tkde.2022.3201058
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发表时间：
2023-08
影响因子：
8.9
通讯作者：
Shuai Zhao;C. Borcea;Yi Chen
中科院分区：
文献类型：
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作者：
Shuai Zhao;C. Borcea;Yi Chen
广告拦截器使用的快速增长导致在线发布商的收入大幅下降。为了缓解这一问题，许多发布商实施了Wall策略，要求广告拦截用户将预期的网页列入白名单。如果用户拒绝，结果是一个损失的情况：用户被拒绝访问内容，出版商无法获得收入。另一种称为AAX的策略是只向用户显示可接受的广告。然而，可接受的广告产生的收入比常规广告少。本文提出了个性化的柜台广告拦截，动态选择一个柜台广告拦截策略为个人用户。为了实现它，我们提出了一种新的基于深度学习的白名单预测模型。Adblock用户预测白名单页面收到墙策略;其他人收到AAX策略。用于白名单预测的Deep Ad-Block Whitelist Network（DAWN）使用深度学习机制捕获页面特征，用户对页面的兴趣及其对广告的敏感度，反映在历史行为中。此外，DAWN利用白名单预测和停留时间预测的多任务学习来提高性能。DAWN的有效性在福布斯媒体提供的真实数据集上得到了验证。实验结果表明，所提出的反广告拦截政策的优势，现有的政策，创收和用户参与。
The fast increase in ad-blocker usage has resulted in significant revenue loss for online publishers. To mitigate this, many publishers implement the Wall strategy, where an adblock user is asked to whitelist the intended webpage. If the user refuses, the result is a loss-loss situation: the user is denied access to content, and the publisher cannot receive revenue. An alternative strategy, called AAX, is to show only acceptable ads to users. However, acceptable ads generate less revenue than regular ads. This article proposes personalized counter ad-blocking that dynamically chooses a counter ad-blocking strategy for individual users. To implement it, we propose a novel deep learning-based whitelist prediction model. Adblock users predicted to whitelist a page receive the Wall strategy; the others receive the AAX strategy. The proposed Deep Ad-Block Whitelist Network (DAWN) for whitelist prediction captures page characteristics, user interests in pages and their sensitivity to ads, reflected in historic behavior, using a deep learning mechanism. Furthermore, DAWN leverages multi-task learning on whitelist prediction and dwell-time prediction to boost performance. DAWN's effectiveness is validated on a real-world dataset provided by Forbes Media. The experimental results demonstrate the advantages of the proposed counter ad-blocking policy over existing policies on revenue generation and user engagement.