Sequential recommender systems are an important and demanded area of research. Such systems aim to use the order of interactions in a user's history to predict future interactions. The premise is that the order of interactions and sequential patterns play an essential role. Therefore, it is crucial to use datasets that exhibit a sequential structure to evaluate sequential recommenders properly. We apply several methods based on the random shuffling of the user's sequence of interactions to assess the strength of sequential structure across 15 datasets, frequently used for sequential recommender systems evaluation in recent research papers presented at top-tier conferences. As shuffling explicitly breaks sequential dependencies inherent in datasets, we estimate the strength of sequential patterns by comparing metrics for shuffled and original versions of the dataset. Our findings show that several popular datasets have a rather weak sequential structure.

翻译：序列推荐系统是当前研究的重要且需求旺盛的领域。此类系统旨在利用用户历史交互的顺序来预测未来的交互行为，其基本前提是交互顺序与序列模式发挥着关键作用。因此，使用具有序列结构的数据集来正确评估序列推荐系统至关重要。我们基于用户交互序列的随机重排方法，对近年来顶级会议研究论文中常用于序列推荐系统评估的15个数据集进行了序列结构强度分析。由于重排操作会显式破坏数据集中固有的序列依赖关系，我们通过比较数据集原始版本与重排版本在评估指标上的差异来量化序列模式的强度。研究结果表明，多个常用数据集仅表现出较弱的序列结构。