Proactive Recommender Systems in Automotive Scenarios

This thesis investigates proactive recommender systems to avoid information overload inside a car. The proposed system delivers context-adaptive items in the right situation. Explicit explanations are used to make the system comprehensible because it works without user request. To show the applicability of our system, we investigate the acceptance of the drivers. The results show that the drivers tend to accept such a system.