Discerning Canonical User Representation for Cross-Domain Recommendation

Cross-domain recommender systems (CDRs) aim to enhance recommendation outcomes by information transfer across different domains. Existing CDRs have investigated the learning of both domain-specific and domain-shared user preferences to enhance recommendation performance. However, these models typically allow the disparities between shared and distinct user preferences to emerge freely in any space, lacking sufficient constraints to identify differences between two domains and to ensure that both domains are considered simultaneously. Canonical Correlation Analysis (CCA) has shown promise for transferring information between domains. However, CCA only models domain similarities and fails to capture the potential differences between user preferences in different domains. We propose Discerning Canonical User Representation for Cross-Domain Recommendation (DiCUR-CDR) that learns domain-shared and domain-specific user representations simultaneously considering both domains' latent spaces. DiCUR-CDR introduces Discerning Canonical Correlation (DisCCA) user representation learning, a novel design of non-linear CCA for mapping user representations. Unlike prior CCA models that only model the domain-shared multivariate representations by finding their linear transformations, DisCCA uses the same transformations to discover the domain-specific representations too. We compare DiCUR-CDR against several state-of-the-art approaches using two real-world datasets and demonstrate the significance of separately learning shared and specific user representations via DisCCA.