<p>With the popularity of Internet services and the explosive growth of information, recommendation algorithm have become increasingly crucial in the all-media era. However, existing single-domain recommendation algorithms mainly rely on user-item interactions to construct user representations, which limits their ability to capture higher-order information about users and items. To address these challenges, we propose an entity-relation pair attention-based representation with knowledge graph for media-content cross-domain recommendation (EPAR-MCDR). In this framework, we adopt knowledge graph embedding to learning entity representation, which maps nodes and edges to a low-dimensional vector space to form an initial embedding of target domain. To overcome the limitations of single-domain data sparsity, we introduce a domain adaptation technique. It effectively utilizes the rich data in source domain through min–max game between the predictor and the user and item domain discriminator. The proposed method is compared and analyzed with three real-world datasets. Extensive experiments show that the method has effective improvement in the cross-domain recommendation.</p>