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.
翻译:我们展示了一部新的电影和电视节目建议数据集,该数据集来自MDS Kion视频点播平台的真正用户。与其他流行的电影建议数据集(如MovieLens或Netflix)相比,我们的数据集是基于在观察时登记的隐性互动,而不是明确的评级。我们还提供了丰富的背景和侧面信息,包括互动特征(如时间信息、观察时间和观察百分比)、用户人口和丰富的电影元信息。此外,我们描述了MDS Kion挑战(基于该数据集的在线推荐者系统挑战),并概述了获胜者的最佳表现解决方案。我们开放了竞争沙箱,因此欢迎研究人员尝试自己的推荐算法,衡量数据集私处的质量。