基于内容的视频相关性预测挑战：数据、协议和基线
Content-based Video Relevance Prediction Challenge: Data, Protocol, and Baseline
摘要：
视频相关性预测是在线流媒体服务的重要任务之一。考虑到视频和观众反馈的相关性，系统可以提供个性化的推荐，这将有助于用户发现更多感兴趣的内容。在大多数在线服务中，视频相关性表的计算是基于用户的隐式反馈，例如观看和搜索历史。然而，这种方法对于“冷启动”问题表现不佳-当新视频被添加到库中时，推荐系统需要在用户行为知之甚少的情况下引导视频相关性分数。一种很有前途的解决方法是分析视频内容本身，即通过视频帧、音频、字幕和元数据预测视频相关性。在本文中，我们描述了Hulu在2018年ACM多媒体会议上主办的基于内容的视频相关性预测（CBVRP）的挑战。在这一挑战中，Hulu推动了对直接从原始视频中利用内容特征进行视频相关性预测的开放问题的研究。我们提供了大量的视频资产和地面实况相关性从我们的真实系统，建立一个共同的平台，算法开发和性能评估。
英文摘要：
Video relevance prediction is one of the most important tasks for online streaming service. Given the relevance of videos and viewer feedbacks, the system can provide personalized recommendations, which will help the user discover more content of interest. In most online service, the computation of video relevance table is based on users' implicit feedback, e.g. watch and search history. However, this kind of method performs poorly for "cold-start" problems - when a new video is added to the library, the recommendation system needs to bootstrap the video relevance score with very little user behavior known. One promising approach to solve it is analyzing video content itself, i.e. predicting video relevance by video frame, audio, subtitle and metadata. In this paper, we describe a challenge on Content-based Video Relevance Prediction (CBVRP) that is hosted by Hulu in the ACM Multimedia Conference 2018. In this challenge, Hulu drives the study on an open problem of exploiting content characteristics directly from original video for video relevance prediction. We provide massive video assets and ground truth relevance derived from our really system, to build up a common platform for algorithm development and performance evaluation.
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。
