Abstract

ABSTRACT The cold start problem, frequent with recommender systems, addresses the issue in cases where we don't know enough about our users (e.g., the user hasn't rated anything yet, or there are no user activities) in that specific domain. In our paper we present a simple and robust transfer learning approach where we model users' behavior in a source domain, transferring that knowledge to a new, target domain. First, we vectorize the items by using word2vec for each dataset independently. Second, we calculate the transformation matrix that connects the source dataset to the target dataset by using their common users.