Abstract
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.
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Notes
The dataset available to download on Github: https://github. com/MobileTeleSystems/RecTools/tree/main/datasets/KION.
The only reprocessing we do is anonymisation and adding a small amount of noise in order to preserve the privacy of Kion’s users.
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ACKNOWLEDGMENTS
We would would like to acknowledge Kion challenge participants Oleg Lashinin, Stepan Zimin, and Olga for providing descriptions of their Kion Challenge solutions, MTS Holding for providing the Kion dataset, ODS.ai international platform for hosting the competition. The contribution of Ildar Safilo and D.I. Ignatov to the article was done within the framework of the HSE University Basic Research Program.
Funding
This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.
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Safilo, I., Tikhonovich, D., Petrov, A.V. et al. MTS Kion Implicit Contextualised Sequential Dataset for Movie Recommendation. Dokl. Math. 108 (Suppl 2), S456–S464 (2023). https://doi.org/10.1134/S1064562423701594
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DOI: https://doi.org/10.1134/S1064562423701594