  We present a new movie and TV show recommendation dataset collected from the
real users of MTS Kion video-on-demand platform. In contrast to other popular
movie recommendation datasets, such as MovieLens or Netflix, our dataset is
based on the implicit interactions registered at the watching time, rather than
on explicit ratings. We also provide rich contextual and side information
including interactions characteristics (such as temporal information, watch
duration and watch percentage), user demographics and rich movies
meta-information. In addition, we describe the MTS Kion Challenge - an online
recommender systems challenge that was based on this dataset - and provide an
overview of the best performing solutions of the winners. We keep the
competition sandbox open, so the researchers are welcome to try their own
recommendation algorithms and measure the quality on the private part of the
dataset.
