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 user's 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.
|Title of host publication||Cooperative design, visualization, and engineering|
|Subtitle of host publication||13th International Conference, CDVE 2016, NSW, Australia, October 24–27, 2016, Proceedings|
|Place of Publication||Switzerland|
|Number of pages||8|
|Publication status||Published - 2016|
|Name||Lecture Notes in Computer Science|
Saleheen, S., Lai, W., Huang, X., Huang, W., & Huang, M. L. (2016). What next in designing personalized visualization of web information. In Y. Luo (Ed.), Cooperative design, visualization, and engineering: 13th International Conference, CDVE 2016, NSW, Australia, October 24–27, 2016, Proceedings (pp. 134-141). (Lecture Notes in Computer Science; Vol. 9929). Springer. https://doi.org/10.1007/978-3-319-46771-9_18