  Data sparsity and data imbalance are practical and challenging issues in
cross-domain recommender systems. This paper addresses those problems by
leveraging the concepts which derive from representation learning, adversarial
learning and transfer learning (particularly, domain adaptation). Although
various transfer learning methods have shown promising performance in this
context, our proposed novel method RecSys-DAN focuses on alleviating the
cross-domain and within-domain data sparsity and data imbalance and learns
transferable latent representations for users, items and their interactions.
Different from existing approaches, the proposed method transfers the latent
representations from a source domain to a target domain in an adversarial way.
The mapping functions in the target domain are learned by playing a min-max
game with an adversarial loss, aiming to generate domain indistinguishable
representations for a discriminator. Four neural architectural instances of
ResSys-DAN are proposed and explored. Empirical results on real-world Amazon
data show that, even without using labeled data (i.e., ratings) in the target
domain, RecSys-DAN achieves competitive performance as compared to the
state-of-the-art supervised methods. More importantly, RecSys-DAN is highly
flexible to both unimodal and multimodal scenarios, and thus it is more robust
to the cold-start recommendation which is difficult for previous methods.
