论文标题
轻松的n对损失电视内容的上下文感知建议
Relaxed N-Pairs Loss for Context-Aware Recommendations of Television Content
论文作者
论文摘要
本文通过提出一种基于深度学习的联合背景嵌入（JCCE）的深度学习方法来研究电视领域中的情境意识建议。该方法基于建议使用潜在表示和深度度量学习的建议，以有效地表示查看情况的上下文设置以及共享潜在空间中的可用内容。该嵌入空间用于通过应用N对损失目标以及本文提出的轻松变体来探索各种观看设置中的相关内容。实验证实了JCCE的建议能力，与最先进的方法相比，实现了改进。进一步的实验在学习的嵌入式中显示有用的结构，可用于获得上下文设置和内容属性之间关系中基本变量的宝贵知识。
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 proposed in this paper. Experiments 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 for gaining valuable knowledge of underlying variables in the relationship between contextual settings and content properties.
