Today, personalization in information systems occurs separately within each system that one interacts with. However, there are several potential improvements w.r.t.~such isolated approaches. Thus, investments of users in personalizing a system, either through explicit provision of information, or through long and regular use are not transferable to other systems. Moreover, users have little or no control over their profile, since it is deeply buried in personalization engines. Cross-system personalization, i.e.~personalization that shares personal information across different systems in a user-centric way, overcomes these problems. User profiles, which are originally scattered across multiple systems, are combined to obtain maximum leverage. This paper discusses an approach in support of cross-system personalization, where a large number of users cross from one system to another, carrying their user profiles with them. These sets of corresponding profiles can be used to learn a mapping between the user profiles of the two systems. In this work, we present and evaluate the use of factor analysis for the purpose of computing recommendations for a new user crossing over from one system to another.