RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems
This addresses cold-start recommendation challenges in cross-domain systems, offering a flexible solution for unimodal and multimodal scenarios, though it is incremental as it builds on existing transfer learning concepts.
The paper tackles data sparsity and imbalance in cross-domain recommender systems by proposing RecSys-DAN, a method that uses adversarial learning to transfer latent representations without target domain labels, achieving competitive performance with state-of-the-art supervised methods on Amazon data.
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.