Abstract
A challenge of Context-Aware Recommender Systems (CARSs) is the cold-start problem, i.e., the usual poor recommendation of new items to new users in new contextual situations. In this research, we aim at solving this problem by developing a switching hybrid CARS, which exploits different context-aware recommendation techniques, each of which has its own strengths and weaknesses, and switches between these techniques depending on the current recommendation situation (i.e., new user, new item and/or new context).