  Recently multi-domain recommender systems have received much attention from
researchers because they can solve cold-start problem as well as support for
cross-selling. However, when applying into multi-domain items, although
algorithms specifically addressing a single domain have many difficulties in
capturing the specific characteristics of each domain, multi-domain algorithms
have less opportunity to obtain similar features among domains. Because both
similarities and differences exist among domains, multi-domain models must
capture both to achieve good performance. Other studies of multi-domain systems
merely transfer knowledge from the source domain to the target domain, so the
source domain usually comes from external factors such as the search query or
social network, which is sometimes impossible to obtain. To handle the two
problems, we propose a model that can extract both homogeneous and divergent
features among domains and extract data in a domain can support for other
domain equally: a so-called Domain-to-Domain Translation Model (D2D-TM). It is
based on generative adversarial networks (GANs), Variational Autoencoders
(VAEs), and Cycle-Consistency (CC) for weight-sharing. We use the user
interaction history of each domain as input and extract latent features through
a VAE-GAN-CC network. Experiments underscore the effectiveness of the proposed
system over state-of-the-art methods by a large margin.
