Abstract in English:

In this paper, we present a framework describing the various aspects of recommender systems that can serve for empowering users by giving them more interactive control and transparency in the recommendation process. While conventional recommenders mostly operate like black boxes that cannot be influenced by the user, we identify four aspects properly connected with the recommendation algorithm—namely input data, user model, external con-text model and presentation—as essential points in which a system may be enhanced by additional interaction possibilities. In light of this framework, we take a closer look at prior and present solutions to integrate recommender systems with more interactivity and describe future research challenges. Regarding these challenges, we especially focus on experiences gained in our own work and outline future research we have planned in the area of interactive recommending.