Adaptation and Recommendation in Modern Web 2.0 Portals

Abstract

In this paper, we propose a generic recommender framework that allows transparently integrating different recommender engines into a Portal. The framework comes with a number of preinstalled recommender engines and can be extended by adding further such components. Recommendations are computed by each engine and then transparently merged. This ensures that neither the Portal vendor, nor the Portal operator, nor the user is burdened with choosing an appropriate engine and still high quality recommendations can be made. Furthermore we present means to automatically adapt the Portal system to better suit users needs.