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.

通过视频信息的音频、文字、帧和元数据等特征，预测视频的相关度，以提供个性化的推荐，并解决推荐系统中的新视频问题，其中Hulu在ACM Multimedia Conference 2018主办了挑战赛，提供了海量视频资源和基于真实系统的相关数据来评估算法表现。

基于内容的视频相关度预测挑战：数据，协议和基线