Personalized peer filtering for a dynamic information push

Authors:
Melanie Gnasa;Sascha Alda;Nadir Gül;Armin B. Cremers
Affiliations:
Institute of Computer Science III, University of Bonn, Bonn, Germany;Institute of Computer Science III, University of Bonn, Bonn, Germany;Institute of Computer Science III, University of Bonn, Bonn, Germany;Institute of Computer Science III, University of Bonn, Bonn, Germany
Venue:
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Year:
2005

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Abstract

Due to the anonymity of the user during Web searching, no support for long-term information needs exists. First attempts for personalized Web retrieval are made, however these approaches are limited to static objects and no individual recommendations from a dynamic data set can be determined. Peer-to-peer architectures build a promising platform for a personalized information filtering system, where all steps during information exchange are transparent to the user. Our approach assists active requests in the form of an information pull as well as a system initiated information push. In a cooperative manner all peers have the function of information providers and consumers. The ranking of recommendations is established by a community-based filtering approach.