Video recommendation has become an essential way of helping people explore the massive videos and discover the ones that may be of interest to them. In the existing video recommender systems, the models make the recommendations based on the user-video interactions and single specific content features. When the specific content features are unavailable, the performance of the existing models will seriously deteriorate. Inspired by the fact that rich contents (e.g., text, audio, motion, and so on) exist in videos, in this paper, we explore how to use these rich contents to overcome the limitations caused by the unavailability of the specific ones. Specifically, we propose a novel general framework that incorporates arbitrary single content feature with user-video interactions, named as collaborative embedding regression (CER) model, to make effective video recommendation in both in-matrix and out-of-matrix scenarios. Our extensive experiments on two real-world large-scale datasets show that CER beats the existing recommender models with any single content feature and is more time efficient. In addition, we propose a priority-based late fusion (PRI) method to gain the benefit brought by the integrating the multiple content features. The corresponding experiment shows that PRI brings real performance improvement to the baseline and outperforms the existing fusion methods.

中文翻译：

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使用视频中的丰富内容进行个性化视频推荐

视频推荐已成为帮助人们探索海量视频并发现他们可能感兴趣的重要方式。在现有的视频推荐系统中，模型基于用户-视频交互和单个特定内容特征进行推荐。当特定的内容特征不可用时，现有模型的性能将严重恶化。受视频中存在丰富的内容（如文本、音频、动作等）的启发，本文探索如何利用这些丰富的内容来克服特定内容不可用带来的局限性。具体来说，我们提出了一种新颖的通用框架，将任意单个内容特征与用户视频交互结合起来，称为协作嵌入回归（CER）模型，在矩阵内和矩阵外场景中进行有效的视频推荐。我们在两个真实世界的大规模数据集上进行的大量实验表明，CER 击败了具有任何单一内容特征的现有推荐模型，并且时间效率更高。此外，我们提出了一种基于优先级的后期融合（PRI）方法，以获得集成多个内容特征所带来的好处。相应的实验表明，PRI 为基线带来了真正的性能提升，并且优于现有的融合方法。我们提出了一种基于优先级的后期融合（PRI）方法，以获得集成多个内容特征所带来的好处。相应的实验表明，PRI 为基线带来了真正的性能提升，并且优于现有的融合方法。我们提出了一种基于优先级的后期融合（PRI）方法，以获得集成多个内容特征所带来的好处。相应的实验表明，PRI 为基线带来了真正的性能提升，并且优于现有的融合方法。