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Cited by 270

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Detecting web page structure for adaptive viewing on small form factor devices

Chen, Yu, Ma, Wei-Ying, Zhang, Hong-Jiang·2003·Proceedings of the twelfth international conference on World Wide Web - WWW '03

Abstract

Mobile devices have already been widely used to access the Web. However, because most available web pages are designed for desktop PC in mind, it is inconvenient to browse these large web pages on a mobile device with a small screen. In this paper, we propose a new browsing convention to facilitate navigation and reading on a small-form-factor device. A web page is organized into a two level hierarchy with a thumbnail representation at the top level for providing a global view and index to a set of sub-pages at the bottom level for detail information. A page adaptation technique is also developed to analyze the structure of an existing web page and split it into small and logically related units that fit into the screen of a mobile device. For a web page not suitable for splitting, auto-positioning or scrolling-by-block is used to assist the browsing as an alterative. Our experimental results show that our proposed browsing convention and developed page adaptation scheme greatly improve the user's browsing experiences on a device with a small display.

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Metadata

Authors
Chen, Y., Ma, W.-Y., & Zhang, H.-J.
Venue
Proceedings of the twelfth international conference on World Wide Web - WWW '03
Publisher
ACM Press
Published
2003
Pages
225
OpenAlex
W2147428516
Cited by
270
References
0
Type
conference abstract
Language
en
Open access
closed

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CoChat. (2026). Citation check: Detecting web page structure for adaptive viewing on small form factor devices. https://app.cochat.ai/papers/10.1145/775152.775184
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Chen, Y., Ma, W.-Y., & Zhang, H.-J. (2003). Detecting web page structure for adaptive viewing on small form factor devices. Proceedings of the twelfth international conference on World Wide Web - WWW '03. https://doi.org/10.1145/775152.775184

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.