Recommender systems predict items a user is likely to like using historical data about users and items. A key challenge is how to provide recommendations when historical data is sparse or missing, known as the cold-start problem. Current solutions to this problem
assume that given an item and a user, the recommendation process misses historical data only about one of them but not both. In this paper, we are interested in the challenging more severe form of the cold-start problem of new-user/new-item. In particular, we are in-
terested in cases where a system collects historical data about users and items but produces recommendations mostly for new or anonymous users and about new or evolved items. We present methods that can be used to deal with this problem, study them and present our experimental findings.