论文标题
MTS Kion隐式上下文化的顺序数据集用于电影推荐
MTS Kion Implicit Contextualised Sequential Dataset for Movie Recommendation
论文作者
论文摘要
我们介绍了一个新的电影和电视节目推荐数据集，该数据集是从MTS Kion视频按需平台的真实用户那里收集的。与其他流行的电影推荐数据集相反，例如Movielens或Netflix，我们的数据集基于观看时间注册的隐式交互，而不是基于显式评分。我们还提供丰富的上下文和附带信息，包括互动特征（例如时间信息，观看持续时间和观看百分比），用户人口统计和丰富的电影元信息。此外，我们描述了MTS Kion Challenge - 基于此数据集的在线推荐系统挑战 - 并概述了获奖者的最佳性能解决方案。我们保持竞争沙箱的打开状态，因此欢迎研究人员尝试自己的建议算法并测量数据集私有部分的质量。
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.
