Can we predict the Most Replayed data of video streaming platforms?
Alessandro Duico Ombretta Strafforello Jan van Gemert
Predicting which specific parts of a video users will replay is important for
several applications, including targeted advertisement placement on video
platforms and assisting video creators. In this work, we explore whether it is
possible to predict the Most Replayed (MR) data from YouTube videos. To this
end, we curate a large video benchmark, the YTMR500 dataset, which comprises
500 YouTube videos with MR data annotations. We evaluate Deep Learning (DL)
models of varying complexity on our dataset and perform an extensive ablation
study. In addition, we conduct a user study to estimate the human performance
on MR data prediction. Our results show that, although by a narrow margin, all
the evaluated DL models outperform random predictions. Additionally, they
exceed human-level accuracy. This suggests that predicting the MR data is a
difficult task that can be enhanced through the assistance of DL. Finally, we
believe that DL performance on MR data prediction can be further improved, for
example, by using multi-modal learning. We encourage the research community to
use our benchmark dataset to further investigate automatic MR data prediction.