“KWARC was!”

Presentation by Andreas Nauerz (IBM, University of Jena) at Lernen, Wissen, Adaptivität (LWA 2008), University of Würzburg, 6.-8. October 2008. Track: ABIS (see paper)

Focus: Improving accessibility of web portals. Company web portals (Enterprise Information Portals) often include immense corpora of contents, which are hardly ever used by the user as selection and navigation is often too tedious. Main concepts of the authors have been integrated in the IBM WebSphere Portal.

The author propose a complex user and context model (date, time, location). The user model reflect the user’s interest and preference: Information from static profiles (native language, home country, working location, age, …), the user’s interaction behaviour (pages and portlets they work with, tags users apply to resources); and the user’s social networks are used to derive knowledge on the user’s needs. For example, the user model includes information on the tagging, rating, and commenting behaviour of users: Tagging and rating behaviour are analysed to understand interest and preference of single users and entire communities. The static data is entered by the user, while more dynamic data is extracted from the user interaction using web usage mining.

Based on the user models two main services are provided:

• Content adaptation: navigation and page layouts (improve accessibility of contents that users frequently use; make more content accessible but adapting its structure/ layout and make its relevance obvious to the user)
• Content recommendation: based on background information, related content, or activities of experts and similar behaving users (make recommendation of new material, which relevance is not obvious to the user)

Three independent context profile are created: (1) travelling, (2) office, (3) at home: User activities during are only stored in the respective context. For example, activity during travelling do not influence the user modelling for office work or at home.

Tags can be associate to web pages, documents, fragments of pages (very granular). They can be typed by the users or their semantics can be automatically extracted by calling respective back-end services.

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