  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.
