  Cross domain recommender systems have been increasingly valuable for helping
consumers identify the most satisfying items from different categories.
However, previously proposed cross-domain models did not take into account
bidirectional latent relations between users and items. In addition, they do
not explicitly model information of user and item features, while utilizing
only user ratings information for recommendations. To address these concerns,
in this paper we propose a novel approach to cross-domain recommendations based
on the mechanism of dual learning that transfers information between two
related domains in an iterative manner until the learning process stabilizes.
We develop a novel latent orthogonal mapping to extract user preferences over
multiple domains while preserving relations between users across different
latent spaces. Combining with autoencoder approach to extract the latent
essence of feature information, we propose Deep Dual Transfer Cross Domain
Recommendation (DDTCDR) model to provide recommendations in respective domains.
We test the proposed method on a large dataset containing three domains of
movies, book and music items and demonstrate that it consistently and
significantly outperforms several state-of-the-art baselines and also classical
transfer learning approaches.
