부산대학교 도서관

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.