  One of the main challenges in Recommender Systems (RSs) is the New User
problem which happens when the system has to generate personalised
recommendations for a new user whom the system has no information about. Active
Learning tries to solve this problem by acquiring user preference data with the
maximum quality, and with the minimum acquisition cost. Although there are
variety of works in active learning for RSs research area, almost all of them
have focused only on the single-domain recommendation scenario. However,
several real-world RSs operate in the cross-domain scenario, where the system
generates recommendations in the target domain by exploiting user preferences
in both the target and auxiliary domains. In such a scenario, the performance
of active learning strategies can be significantly influenced and typical
active learning strategies may fail to perform properly. In this paper, we
address this limitation, by evaluating active learning strategies in a novel
evaluation framework, explicitly suited for the cross-domain recommendation
scenario. We show that having access to the preferences of the users in the
auxiliary domain may have a huge impact on the performance of active learning
strategies w.r.t. the classical, single-domain scenario.
