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Relaxed N-Pairs Loss for Context-Aware Recommendations of Television Content
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 proposed in this paper.
Deep Joint Embeddings of Context and Content for Recommendation
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.
Subjective Annotations for Vision-Based Attention Level Estimation
Attention level estimation systems have a high potential in many use cases, such as human-robot interaction, driver modeling and smart home systems, since being able to measure a person's attention level opens the possibility to natural interaction between humans and computers.
The Importance of Context When Recommending TV Content: Dataset and Algorithms
Home entertainment systems feature in a variety of usage scenarios with one or more simultaneous users, for whom the complexity of choosing media to consume has increased rapidly over the last decade.
