Collaborative deep recommendation with global and local item correlations

Abstract

Many recommendation methods have introduced item correlation information to alleviate the data sparsity and cold-start problems. However, existing approaches exploit either global or local item correlations, rarely consider both global and local item correlations, and thus they cannot provide advanced recommendation performance. Inspired by this, we propose a novel collaborative deep framework called GLICR to simultaneously incorporate the global and local item correlations into the model. More specifically, our proposed GLICR model tightly couples deep neural network with matrix factorization (MF), and jointly learns the deep feature representations of item content information in deep neural network and the rating matrix in MF. In addition, we introduce manifold regularization to learn the global and local item correlations directly from data. We conduct comprehensive experiments on real-world datasets at three different degrees of sparsity to confirm that our approach can effectively alleviate data sparsity problem and is superior to existing state-of-the-art recommendation techniques. This work is the first attempt that considers the global and local item correlations by manifold regularization in recommendation scenario.