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Context-Aware Recommendations for Televisions Using Deep Embeddings with Relaxed N-Pairs Loss Objective
This paper studies context-aware recommendations in the television domain by
proposing a deep learning-based method for learning joint context-content
embeddings (JCCE). The method builds on recent developments within
recommendations using latent representations and deep metric learning, in order
to effectively represent contextual settings of viewing situations as well as
available content in a shared latent space. This embedding space is used for
exploring relevant content in various viewing settings by applying an N -pairs
loss objective as well as a relaxed variant introduced in this paper.
Experiments on two datasets confirm the recommendation ability of JCCE,
achieving improvements when compared to state-of-the-art methods. Further
experiments display useful structures in the learned embeddings that can be
used to gain valuable knowledge of underlying variables in the relationship
between contextual settings and content properties