Journal article
Dynamic content-page identification for media-rich websites
Multimedia tools and applications, Vol.50(3), pp.491-507
12/01/2010
DOI: 10.1007/s11042-010-0487-1
Abstract
Knowledge of the information goal of users is critical in website design, analyzing the efficacy of such designs, and in ensuring effective user-access to desired information. Determining the information goal is complex due to the subjective and latent nature of user information needs. This challenge is further exacerbated in media-rich websites since the semantics of media-based information is context-based and emergent. A critical step in determining information goals lies in the identification of content pages. These are the pages which contain the information the user seeks. We propose a method to automatically determine the content pages by taking into account the organization of the web site, the media-based information content, as well as the influence of a specific user browsing pattern. Given a specific browsing pattern, in our method, putative content pages are identified as the pages corresponding to the local minima of page-content entropy values. For an (unknown) user information goal this intuitively corresponds to modeling the progressive transition of the user from pages with generic information to those with specific information. Experimental investigations on media rich sites demonstrate the effectiveness of the technique and underline its potential in modeling user information needs and actions in a media-rich web.
Details
- Title: Subtitle
- Dynamic content-page identification for media-rich websites
- Creators
- Rahul Singh - San Francisco State UniversityBibek D. Bhattarai - San Francisco State University
- Resource Type
- Journal article
- Publication Details
- Multimedia tools and applications, Vol.50(3), pp.491-507
- Publisher
- Springer Nature
- DOI
- 10.1007/s11042-010-0487-1
- ISSN
- 1380-7501
- eISSN
- 1573-7721
- Number of pages
- 17
- Grant note
- Microsoft
- Language
- English
- Date published
- 12/01/2010
- Academic Unit
- Computer Science
- Record Identifier
- 9984446555502771
Metrics
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