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This paper studies context-aware recommendations in the television domain byproposing a deep learning-based method for learning joint context-contentembeddings (JCCE). The method builds on recent developments withinrecommendations using latent representations and deep metric learning, in orderto effectively represent contextual settings of viewing situations as well asavailable content in a shared latent space. This embedding space is used forexploring relevant content in various viewing settings by applying an N-pairsloss objective as well as a relaxed variant proposed in this paper. Experimentsconfirm the recommendation ability of JCCE, achieving improvements whencompared to state-of-the-art methods. Further experiments display usefulstructures in the learned embeddings that can be used for gaining valuableknowledge of underlying variables in the relationship between contextualsettings and content properties.

Relaxed N-Pairs Loss for Context-Aware Recommendations of Television Content