ISKODOR is an experimental search system developed a the University of Bonn, which goal is the implementation of the ‘congenial web search’, a user-centered approach where search quality is constantly evaluated through explicit feedback.

ISKODOR implements personalized ranking matrices; collaborative information retrieval in the form of peer groups, which are used to limit the scope of a search.

The Web provides a global platform for knowledge sharing. However, several shortcomings still arise from the absence of personalization and collaboration in Web searches. More effective retrieval techniques could be provided by means of transforming explicit knowledge into implicit knowledge. Iskodor is based on a peer-to-peer architecture and aims at complementing classical Web searches in terms of personalized ranking lists. These local rankings can be accumulated and evaluated in order to supplement the process of knowledge generation by building Virtual Knowledge Communities. Furthermore, the aggregation of ranking lists can be used to identify topics as well as communities of interest. Together with social aspects for community support, a framework for congenial Web search is defined.

Tags: collaboration tools, information retrieval, machine learning