Layout-aware Webpage Quality Assessment

• URL: http://arxiv.org/abs/2301.12152v1
• Date: Sat, 28 Jan 2023 10:27:53 GMT
• Title: Layout-aware Webpage Quality Assessment
• Authors: Anfeng Cheng, Yiding Liu, Weibin Li, Qian Dong, Shuaiqiang Wang, Zhengjie Huang, Shikun Feng, Zhicong Cheng and Dawei Yin
• Abstract summary: We propose a novel layout-aware webpage quality assessment model currently deployed in our search engine. We employ the meta-data that describes a webpage, i.e., Document Object Model (DOM) tree, as the input of our model. To assess webpage quality from complex DOM tree data, we propose a graph neural network (GNN) based method.
• Score: 31.537331183733837
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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