Internet has brought about a tremendous increase in content of all forms and, in that, video content constitutes the major backbone of the total content being published as well as watched. Thus it becomes imperative for video recommendation engines such as Hulu to look for novel and innovative ways to recommend the newly added videos to their users. However, the problem with new videos is that they lack any sort of metadata and user interaction so as to be able to rate the videos for the consumers. To this effect, this paper introduces the several techniques we develop for the Content Based Video Relevance Prediction (CBVRP) Challenge being hosted by Hulu for the ACM Multimedia Conference 2018. We employ different architectures on the CBVRP dataset to make use of the provided frame and video level features and generate predictions of videos that are similar to the other videos. We also implement several ensemble strategies to explore complementarity between both the types of provided features. The obtained results are encouraging and will impel the boundaries of research for multimedia based video recommendation systems.
翻译：互联网使所有形式的内容都大幅增加,因此,视频内容是正在出版和观看的全部内容的主要主干,因此,像Hulu这样的视频推荐引擎必须寻找创新的新颖方法,向用户推荐新添加的视频。然而,新视频的问题是,它们缺乏任何类型的元数据和用户互动,无法对消费者的视频进行评级。为此,本文件介绍了我们为由Hulu为2018年ACM多媒体会议主办的内容、相关性预测(CBVRP)挑战开发的几种技术。我们在CBVRP数据集上采用了不同的结构,以利用所提供的框架和视频级别特征,并生成与其他视频相类似的视频预测。我们还实施了几项共同战略,探索所提供的两种类型视频之间的互补性。获得的结果令人鼓舞,并将切断基于多媒体视频建议系统的研究界限。
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