用于电视内容的上下文感知推荐的松弛N对损失
Relaxed N-Pairs Loss for Context-Aware Recommendations of Television Content
摘要：
本文通过提出一种基于深度学习的方法来学习联合上下文内容嵌入（JCCE），研究了电视领域的上下文感知推荐。该方法建立在使用潜在表示和深度度量学习的推荐内的最新发展的基础上，以便有效地表示观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放松变体来探索各种观看设置中的相关内容。实验证实了JCCE的推荐能力，实现改进时相比，国家的最先进的方法。进一步的实验显示有用的结构，学习嵌入，可用于获得有价值的知识的基础变量之间的关系的上下文设置和内容属性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
