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Cross-Domain Recommendation (CDR) has been popularly studied to utilizedifferent domain knowledge to solve the cold-start problem in recommendersystems. Most of the existing CDR models assume that both the source and targetdomains share the same overlapped user set for knowledge transfer. However,only few proportion of users simultaneously activate on both the source andtarget domains in practical CDR tasks. In this paper, we focus on the PartiallyOverlapped Cross-Domain Recommendation (POCDR) problem, that is, how toleverage the information of both the overlapped and non-overlapped users toimprove recommendation performance. Existing approaches cannot fully utilizethe useful knowledge behind the non-overlapped users across domains, whichlimits the model performance when the majority of users turn out to benon-overlapped. To address this issue, we propose an end-to-enddual-autoencoder with Variational Domain-invariant Embedding Alignment (VDEA)model, a cross-domain recommendation framework for the POCDR problem, whichutilizes dual variational autoencoders with both local and global embeddingalignment for exploiting domain-invariant user embedding. VDEA first adoptsvariational inference to capture collaborative user preferences, and thenutilizes Gromov-Wasserstein distribution co-clustering optimal transport tocluster the users with similar rating interaction behaviors. Our empiricalstudies on Douban and Amazon datasets demonstrate that VDEA significantlyoutperforms the state-of-the-art models, especially under the POCDR setting.

Exploiting Variational Domain-Invariant User Embedding for Partially Overlapped Cross Domain Recommendation