  Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR) are
two of the promising solutions to address the long-standing data sparsity
problem in recommender systems. They leverage the relatively richer
information, e.g., ratings, from the source domain or system to improve the
recommendation accuracy in the target domain or system. Therefore, finding an
accurate mapping of the latent factors across domains or systems is crucial to
enhancing recommendation accuracy. However, this is a very challenging task
because of the complex relationships between the latent factors of the source
and target domains or systems. To this end, in this paper, we propose a Deep
framework for both Cross-Domain and Cross-System Recommendations, called
DCDCSR, based on Matrix Factorization (MF) models and a fully connected Deep
Neural Network (DNN). Specifically, DCDCSR first employs the MF models to
generate user and item latent factors and then employs the DNN to map the
latent factors across domains or systems. More importantly, we take into
account the rating sparsity degrees of individual users and items in different
domains or systems and use them to guide the DNN training process for utilizing
the rating data more effectively. Extensive experiments conducted on three
real-world datasets demonstrate that DCDCSR framework outperforms the
state-of-the-art CDR and CSR approaches in terms of recommendation accuracy.
