基于上下文的广告拦截使用暹罗神经网络

Shawn Collins, Emily Wu, R. Ning
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引用次数: 0

摘要

本文提出了一种新的基于内容的广告拦截器,以最大限度地减少有效打击推送广告所需的人力。当前的广告拦截器模型维护成本高昂,而且在识别可能在不同网站中扮演不同角色的易混淆图像时并不总是有效。我们研究了通过引入深度学习、基于内容的广告拦截器模型来解决这些问题的可能性。更具体地说,所提出的广告拦截器通过结合给定图像所包含的信息及其来源的网站的内容来识别广告图像。提出的解决方案被原型化并应用于各种流行网站,实现了98%的检测准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Context-based Adblocker using Siamese Neural Network
This paper proposes a new content-based ad-blocker to minimize the amount of human effort required to effectively combat pushed advertisements. Current ad-blocker models are expensive to maintain and not always effective in identifying confusable images that may play different roles across diverse websites. We investigated the possibility of solving these problems with the introduction of a deep learning, content-based ad-blocker model. More specifically, the proposed ad-blocker identifies advertisement images by combining the contained information of a given image and the content of the website it originated from. The proposed solution was prototyped and applied to a diverse selection of popular websites, achieving a detection accuracy of 98%.
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