  This paper proposes a deep learning-based method for learning joint
context-content embeddings (JCCE) with a view to context-aware recommendations,
and demonstrate its application in the television domain. JCCE builds on recent
progress within latent representations for recommendation and deep metric
learning. The model effectively groups viewing situations and associated
consumed content, based on supervision from 2.7 million viewing events.
Experiments confirm the recommendation ability of JCCE, achieving improvements
when compared to state-of-the-art methods. Furthermore, the approach shows
meaningful structures in the learned representations that can be used to gain
valuable insights of underlying factors in the relationship between contextual
settings and content properties.
