Transfer Learning via Feature Selection Based Nonnegative Matrix Factorization

Transfer learning has been successfully used in recommender systems to deal with the data sparsity problem. Existing techniques assume that the source and target domains share the same feature space. This paper proposes a new direction in transfer learning where the source and target domains can have different feature space. The proposed technique, Feature Selection based Nonnegative Matrix Factorization (FSNMF), selects the useful features that can minimize the cost function of the target domain. The features of the source domain are learned using NMF and their importance is measured using the gradient principle. Experiments with real-world datasets show the effectiveness of FSNMF in comparison to state-of-the-art relevant transfer learning techniques.