Towards a user based recommendation strategy for digital ecosystems
In digital ecosystems, the possibility of discovering useful digital objects based on recommendation techniques is one of the most useful tasks that still remains to realize. In this realm, it seems really powerful to have both an a priori user profiling and an a posteriori knowledge, extracted from historical and current user behavior, thus recommender systems can be easily adapted to these environments. In this paper, we present a hybrid strategy that proposes customized recommendations using semantic contents and potentially low-level features of multimedia objects, past behavior of users in terms of usage patterns and user interests expressed by ontologies. We have implemented a prototype that supports our proposed recommendation strategy and a real use of our system based on a 3D interface is shown. Finally, some preliminary experimental results are presented and discussed.
digital ecosystems recommender systems ontologies semantic analysis user based systems