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What next in designing personalized visualization of web information


Saleheen, S and Lai, W and Huang, X and Huang, W and Huang, ML, What next in designing personalized visualization of web information, Lecture Notes in Computer Science 9929: Proceedings of the 13th International Conference on Cooperative Design, Visualization, and Engineering (CDVE 2016), 24-27 October 2016, Sydney, Australia, pp. 134-141. ISBN 978-3-319-46770-2 (2016) [Refereed Conference Paper]

Copyright Statement

Copyright 2016 Springer International Publishing

DOI: doi:10.1007/978-3-319-46771-9_18


Current state of the art in personalized visualization of web information is tailored to provide a better view of how the information is resided and connected to each other inside the internet. With the recent enhancement in information and communication technology, users are provided a very large amount of information when they search for a particular information from a specific website. Studies show that, user can perceive the information in a more better way if they are provided the information with visual representation instead of its textual counterpart. However, to be effective to the users, the visual representation should be specific to the need of a particular user. Research is conducted from various viewpoints to make the visual representation (graph-representation of the web information) more user-specific. To achieve this, filtering and clustering techniques have been applied to web information to make large web graphs to compact ones. Besides, user modeling has been applied to infer the userís need for a specific time and context. These tend to make the navigation of web information easy and effective to the end user. This paper discusses the current progress in graph-based web information visualization and also outlines the scopes of improvements that could benefit the user exploring the desired information from the web space effectively and efficiently.

Item Details

Item Type:Refereed Conference Paper
Keywords:information visualization, webgraph, user model, clustering
Research Division:Information and Computing Sciences
Research Group:Library and information studies
Research Field:Human information interaction and retrieval
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the information and computing sciences
UTAS Author:Huang, W (Dr Tony Huang)
ID Code:112918
Year Published:2016
Deposited By:Information and Communication Technology
Deposited On:2016-12-04
Last Modified:2018-01-31

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