Recommending Background Information and Related Content in Web 2.0 Portals

Abstract

Modern Web 2.0 Portals have become highly collaborative participation platforms. Users do not only retrieve information, they even contribute content. Due to the large number of different users contributing, Web 2.0 sites grow quickly and, most often, in a more uncoordinated way than centrally controlled sites. Finding relevant information can hence become a tedious task. We will demonstrate a solution allowing for the in-place, in-context recommendation of background information with respect to a certain term or topic and for the recommendation of related content being available in the system. Our solution is based on the extraction of enriched units of information which we either gain automatically via unstructured data analysis or by analyzing user-applied annotations. Our main concepts have been embedded and evaluated within IBM’s WebSphere Portal.