In recommendation systems a variation of the cold start problem is a situation where the target user has few-to-none item ratings belonging to the target domain (e.g., movies) to base recommendations on. One way to overcome this is by basing recommendations on items from different domains, for example recommending movies based on the target user's book item ratings. This technique is called cross-domain recommendation. When basing recommendations on a source domain that is different from the target domain a question arises, from which domain should items be chosen? Is there a source domain that is a better predictor for each target domain? Do books better predict a users' taste in movies or perhaps it's their music preferences? In this study we present initial results of work in progress that ranks and maps between pairs of domains based on the ability to create recommendations in domain one using ratings of items from the other domain. The recommendations are made using cross domain collaborative filtering, and evaluated on the social networking profiles of 2148 users. Initial results show that information that is freely available in social networks can be used for cross domain recommendation and that there are differences between the source domains with respect to the quality of the recommendations.