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.
翻译:本文件提出了一种深层次的学习方法,用于学习联合背景内容嵌入内容,以便了解背景情况的建议,并展示其在电视领域的应用情况。该方法借鉴了建议和深度计量学习的潜在代表中最近取得的进展。该模型有效地根据270万次观看活动的监督情况,对情况和相关消费内容进行了分组观察。实验证实了该委员会的建议能力,与最新方法相比,实现了改进。此外,该方法展示了学习表现中有意义的结构,可以利用这些结构获得对背景环境与内容属性之间关系的基本因素的宝贵洞察力。