  Cross domain recommender systems have been increasingly valuable for helping
consumers identify useful items in different applications. However, existing
cross-domain models typically require large number of overlap users, which can
be difficult to obtain in some applications. In addition, they did not consider
the duality structure of cross-domain recommendation tasks, thus failing to
take into account bidirectional latent relations between users and items and
achieve optimal recommendation performance. To address these issues, in this
paper we propose a novel cross-domain recommendation model based on 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. Furthermore,
we combine the dual learning method with the metric learning approach, which
allows us to significantly reduce the required common user overlap across the
two domains and leads to even better cross-domain recommendation performance.
We test the proposed model on two large-scale industrial datasets and six
domain pairs, demonstrating that it consistently and significantly outperforms
all the state-of-the-art baselines. We also show that the proposed model works
well with very few overlap users to obtain satisfying recommendation
performance comparable to the state-of-the-art baselines that use many overlap
users.
