Identifying high-quality webpages is fundamental for real-world search engines, which can fulfil users' information need with the less cognitive burden. Early studies of \emph{webpage quality assessment} usually design hand-crafted features that may only work on particular categories of webpages (e.g., shopping websites, medical websites). They can hardly be applied to real-world search engines that serve trillions of webpages with various types and purposes. In this paper, we propose a novel layout-aware webpage quality assessment model currently deployed in our search engine. Intuitively, layout is a universal and critical dimension for the quality assessment of different categories of webpages. Based on this, we directly employ the meta-data that describes a webpage, i.e., Document Object Model (DOM) tree, as the input of our model. The DOM tree data unifies the representation of webpages with different categories and purposes and indicates the layout of webpages. To assess webpage quality from complex DOM tree data, we propose a graph neural network (GNN) based method that extracts rich layout-aware information that implies webpage quality in an end-to-end manner. Moreover, we improve the GNN method with an attentive readout function, external web categories and a category-aware sampling method. We conduct rigorous offline and online experiments to show that our proposed solution is effective in real search engines, improving the overall usability and user experience.

翻译：识别高质量网页是现实搜索引擎的基础，这能以更低的认知负担满足用户的信息需求。早期关于"网页质量评估"的研究通常设计手工特征，这些特征可能仅适用于特定类型的网页（例如购物网站、医疗网站），难以应用于服务涵盖数万亿不同类型及用途网页的现实搜索引擎。本文提出了一种已部署于我们搜索引擎中的新型布局感知网页质量评估模型。直觉上，布局是评估不同类别网页质量的通用且关键维度。基于此，我们直接采用描述网页的元数据——即文档对象模型（DOM）树——作为模型输入。DOM树数据统一了不同类别和用途网页的表示，并揭示了网页的布局结构。为从复杂的DOM树数据中评估网页质量，我们提出了一种基于图神经网络（GNN）的方法，该方法以端到端方式提取蕴含网页质量的丰富布局感知信息。此外，我们通过注意力读出函数、外部网页类别及类别感知采样方法改进了GNN方法。严格的离线和在线实验表明，我们提出的解决方案在真实搜索引擎中效果显著，提升了整体可用性和用户体验。