  In this paper, we propose the task of \textit{Ranked Video Moment Retrieval}
(RVMR) to locate a ranked list of matching moments from a collection of videos,
through queries in natural language. Although a few related tasks have been
proposed and studied by CV, NLP, and IR communities, RVMR is the task that best
reflects the practical setting of moment search. To facilitate research in
RVMR, we develop the TVR-Ranking dataset, based on the raw videos and existing
moment annotations provided in the TVR dataset. Our key contribution is the
manual annotation of relevance levels for 94,442 query-moment pairs. We then
develop the $NDCG@K, IoU\geq \mu$ evaluation metric for this new task and
conduct experiments to evaluate three baseline models. Our experiments show
that the new RVMR task brings new challenges to existing models and we believe
this new dataset contributes to the research on multi-modality search. The
dataset is available at \url{https://github.com/Ranking-VMR/TVR-Ranking}
