  Cross-Domain Collaborative Filtering (CDCF) provides a way to alleviate data
sparsity and cold-start problems present in recommendation systems by
exploiting the knowledge from related domains. Existing CDCF models are either
based on matrix factorization or deep neural networks. Either of the techniques
in isolation may result in suboptimal performance for the prediction task.
Also, most of the existing models face challenges particularly in handling
diversity between domains and learning complex non-linear relationships that
exist amongst entities (users/items) within and across domains. In this work,
we propose an end-to-end neural network model -- NeuCDCF, to address these
challenges in a cross-domain setting. More importantly, NeuCDCF follows a wide
and deep framework and it learns the representations combinedly from both
matrix factorization and deep neural networks. We perform experiments on four
real-world datasets and demonstrate that our model performs better than
state-of-the-art CDCF models.
