Layout-aware Webpage Quality Assessment
Anfeng Cheng, Yiding Liu, Weibin Li, Qian Dong, Shuaiqiang Wang,, Zhengjie Huang, Shikun Feng, Zhicong Cheng, Dawei Yin

TL;DR
This paper introduces a layout-aware webpage quality assessment model using DOM trees and graph neural networks, improving quality evaluation across diverse webpage categories in search engines.
Contribution
It proposes a novel GNN-based model utilizing DOM metadata for universal webpage quality assessment, with enhancements like attentive readout and category-aware sampling.
Findings
Effective in real search engines, improving usability.
Outperforms previous methods in quality assessment accuracy.
Enhances user experience through better webpage ranking.
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…
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Taxonomy
TopicsWeb Data Mining and Analysis · Text and Document Classification Technologies · Recommender Systems and Techniques
MethodsGraph Neural Network
