  The conventional single-target Cross-Domain Recommendation (CDR) aims to
improve the recommendation performance on a sparser target domain by
transferring the knowledge from a source domain that contains relatively richer
information. By contrast, in recent years, dual-target CDR has been proposed to
improve the recommendation performance on both domains simultaneously. However,
to this end, there are two challenges in dual-target CDR: (1) how to generate
both relevant and diverse augmented user representations, and (2) how to
effectively decouple domain-independent information from domain-specific
information, in addition to domain-shared information, to capture comprehensive
user preferences. To address the above two challenges, we propose a
Disentanglement-based framework with Interpolative Data Augmentation for
dual-target Cross-Domain Recommendation, called DIDA-CDR. In DIDA-CDR, we first
propose an interpolative data augmentation approach to generating both relevant
and diverse augmented user representations to augment sparser domain and
explore potential user preferences. We then propose a disentanglement module to
effectively decouple domain-specific and domain-independent information to
capture comprehensive user preferences. Both steps significantly contribute to
capturing more comprehensive user preferences, thereby improving the
recommendation performance on each domain. Extensive experiments conducted on
five real-world datasets show the significant superiority of DIDA-CDR over the
state-of-the-art methods.
