NDLI: Evaluating the visual quality of web pages using a computational aesthetic approach

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Evaluating the visual quality of web pages using a computational aesthetic approach

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Evaluating the visual quality of web pages using a computational aesthetic approach

Content Provider	ACM Digital Library
Author	Li, Bing Hu, Weiming Chen, Yunfei Wu, Ou
Abstract	Current Web mining explores useful and valuable information (content) online for users. However, there is scant research on the overall visual aspect of Web pages, even though visual elements such as aesthetics significantly influence user experience. A beautiful and well-laid out Web page greatly facilitates users' accessing and enhances browsing experiences.We use "visual quality (VisQ)" to denote the aesthetics of Web pages. In this paper, a computational aesthetics approach is proposed to learn the evaluation model for the visual quality of Web pages. First, a Web page layout extraction algorithm (V-LBE) is introduced to partition a Web page into major layout blocks. Then, regarding a Web page as a semi-structured image, features (e.g., layout,visual complexity, colorfulness) known to significantly affect the visual quality of a Web page are extracted to construct a feature vector. We present a multi-cost-sensitive learning for visual quality classification and a multi-value regression for visual quality score assignment. Our experiments compare the extracted features and conclude that the Web page's layout visual features (LV) and text visual features (TV) are the primary affecting factors toward Web page's visual quality. The performance of the learned visual quality classifier is close to some persons'. The learned regression function also achieves promising results.
Starting Page	337
Ending Page	346
Page Count	10
File Format	PDF
ISBN	9781450304931
DOI	10.1145/1935826.1935883
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-02-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Learning Visual quality Web mining Aesthetics Semi-structured image
Content Type	Text
Resource Type	Article

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