Sequential recommendation is an important task to predict the next-item to access based on a sequence of interacted items. Most existing works learn user preference as the transition pattern from the previous item to the next one, ignoring the time interval between these two items. However, we observe that the time interval in a sequence may vary significantly different, and thus result in the ineffectiveness of user modeling due to the issue of \emph{preference drift}. In fact, we conducted an empirical study to validate this observation, and found that a sequence with uniformly distributed time interval (denoted as uniform sequence) is more beneficial for performance improvement than that with greatly varying time interval. Therefore, we propose to augment sequence data from the perspective of time interval, which is not studied in the literature. Specifically, we design five operators (Ti-Crop, Ti-Reorder, Ti-Mask, Ti-Substitute, Ti-Insert) to transform the original non-uniform sequence to uniform sequence with the consideration of variance of time intervals. Then, we devise a control strategy to execute data augmentation on item sequences in different lengths. Finally, we implement these improvements on a state-of-the-art model CoSeRec and validate our approach on four real datasets. The experimental results show that our approach reaches significantly better performance than the other 11 competing methods. Our implementation is available: https://github.com/KingGugu/TiCoSeRec.

翻译：序列推荐是一项重要任务，旨在根据用户交互项目序列预测下一个要访问的项目。现有方法大多将用户偏好建模为前一个项目到后一个项目的转移模式，却忽略了这两者之间的时间间隔。然而，我们观察到序列中的时间间隔可能存在显著差异，由此引发的"偏好漂移"问题会导致用户建模失效。为此，我们通过实证研究验证了这一发现，并证明相较于时间间隔变化剧烈的序列，具有均匀分布时间间隔的序列（简称均匀序列）更有利于性能提升。因此，本文首次从时间间隔角度提出序列数据增强方法。具体而言，我们设计了五种操作算子（Ti-Crop、Ti-Reorder、Ti-Mask、Ti-Substitute、Ti-Insert），通过考量时间间隔方差将原始非均匀序列转化为均匀序列。随后，我们设计了针对不同长度项目序列的数据增强控制策略。最后，我们在先进模型CoSeRec上实现这些改进，并在四个真实数据集上验证了所提方法。实验结果表明，我们的方法显著优于其他11种对比方法。实现代码已开源：https://github.com/KingGugu/TiCoSeRec。