Intelligent Knowledge Recommendation Methods for R&D Knowledge Portals

Abstract

The personalization in knowledge portals and knowledge management systems is mainly performed based on users’ explicitly specified categories and keywords. The explicit specification approach requires users’ participation to start personalization services, and has limitation to adapt changes of users’ preference. This paper suggests two implicit personalization approaches: automatic user category assignment method and automatic keyword profile generation method. The performances of the implicit personalization approaches are compared with traditional personalization approach using an Internet news site experiment. The result of the experiment shows that the suggested personalization approaches provide sufficient recommendation effectiveness with lessening users’ unwanted involvement in personalization process.