Collaborative filtering is a popular technique for making high quality recommendations, and it can also recommend items from multiple domains (e.g. books vs. movies). However, it has been found that collaborative filtering makes more accurate recommendations within a domain than across domains. The system in this paper uses the personalities of users to generate the recommendations, by developing a profile for each item that reflects the personality of users who like it. The item profiles are used to make item-based collaborative filtering recommendations. The experiments show that the accuracy becomes greater when the system makes cross-domain recommendations, than when it works in one domain.