Deep Joint Embeddings of Context and Content for Recommendation
This work addresses context-aware recommendations for television viewers, presenting an incremental advancement in the field.
The paper tackles the problem of context-aware recommendations by proposing a deep learning method for joint context-content embeddings (JCCE), achieving improvements over state-of-the-art methods based on experiments with 2.7 million viewing events.
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.