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.

With the explosion of information and users’ changing interest, program sequential recommendation becomes increasingly important for TV program platforms to help their users find interesting programs. Existing sequential recommendation methods mainly focus on modeling user preferences from users’ historical interaction behaviors directly, with insufficient learning about the dynamics of programs and users, while ignoring the rich semantic information from the heterogeneous graph. To address these issues, we propose the multipath-guided heterogeneous graph neural networks for TV program sequential recommendation (MHG-PSR), which can enhance the representations of programs and users through multiple paths in heterogeneous graphs. In our method, the auxiliary information is fused to supplement the semantics of program and user to obtain initial representations. Then, we explore the interactive behaviors of programs and users with temporal and auxiliary information to model the collaborative signals in the heterogeneous graph and extract the users’ dynamic preferences of programs. Extensive experiments on real-world datasets verify the proposed method can effectively improve the performance of TV program sequential recommendation.