Adaptive Portals: Adapting and Recommending Content and Expertise
| dc.contributor.author | Nauerz, Andreas | de_DE |
| dc.contributor.author | Bakalov, Fedor | de_DE |
| dc.contributor.author | König-Ries, Birgitta | de_DE |
| dc.contributor.author | Welsch, Martin | de_DE |
| dc.contributor.editor | Hartmann, Melanie | de_DE |
| dc.contributor.editor | Krause, Daniel | de_DE |
| dc.contributor.editor | Nauerz, Andreas | de_DE |
| dc.date.accessioned | 2017-11-15T15:00:45Z | |
| dc.date.available | 2017-11-15T15:00:45Z | |
| dc.date.issued | 2008 | |
| dc.description.abstract | Today, Portals provide users with a central point of access to companywide information. Initially they focused on presenting the most valuable and widely used information to users providing them with quick and efficient information access. But the amount of information accessible quickly grew and finding the right information became more and more complex and time consuming. In this paper, we illustrate options for adapting and recommending content based on user- and context models that reflect users’ interests and preferences and on annotations of resources provided by users. We additionally leverage the entire communitys’ interests, preferences and collective intelligence to perform group-based adaptation. We adapt a Portal’s structure (e.g. navigation) and provide recommendations to be able to reach content being of interest easier. We recommend background in- formation, experts and users with similar interests. We finally construct a Portal’s navigation structure entirely based on the communitys’ behavior. Our main concepts have been prototypically embedded within IBM’s WebSphere Portal. | |
| dc.identifier.uri | http://abis.l3s.uni-hannover.de/images/proceedings/abis2008/abis2008_nauerz_bakalov.pdf | de_DE |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/5076 | |
| dc.language.iso | en | de_DE |
| dc.relation.ispartof | 16th Workshop on Adaptivity and User Modeling in Interactive Systems | de_DE |
| dc.title | Adaptive Portals: Adapting and Recommending Content and Expertise | de_DE |
| dc.type | Text/Conference Paper | |
| gi.document.quality | digidoc |