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: this https URL

该论文提出了一种基于时间间隔改进的数据增广方法，提高了串行推荐的性能表现，并在四个真实数据集上验证了该方法相较于其他 11 种竞争方法显著更好的性能表现。

时间间隔感知数据增强优化均匀序列：用于序列推荐

Uniform Sequence Better: Time Interval Aware Data Augmentation for  Sequential Recommendation

Shared-account Cross-domain Sequential Recommendation (SCSR) task aims to
recommend the next item via leveraging the mixed user behaviors in multiple
domains. It is gaining immense research attention as more and more users tend
to sign up on different platforms and share accounts with others to access
domain-specific services. Existing works on SCSR mainly rely on mining
sequential patterns via Recurrent Neural Network (RNN)-based models, which
suffer from the following limitations: 1) RNN-based methods overwhelmingly
target discovering sequential dependencies in single-user behaviors. They are
not expressive enough to capture the relationships among multiple entities in
SCSR. 2) All existing methods bridge two domains via knowledge transfer in the
latent space, and ignore the explicit cross-domain graph structure. 3) None
existing studies consider the time interval information among items, which is
essential in the sequential recommendation for characterizing different items
and learning discriminative representations for them. In this work, we propose
a new graph-based solution, namely TiDA-GCN, to address the above challenges.
Specifically, we first link users and items in each domain as a graph. Then, we
devise a domain-aware graph convolution network to learn userspecific node
representations. To fully account for users' domainspecific preferences on
items, two effective attention mechanisms are further developed to selectively
guide the message passing process. Moreover, to further enhance item- and
account-level representation learning, we incorporate the time interval into
the message passing, and design an account-aware self-attention module for
learning items' interactive characteristics. Experiments demonstrate the
superiority of our proposed method from various aspects.

本文提出了一种名为 TiDA-GCN 的图形解决方案，以解决跨域顺序推荐中的挑战，包括多个实体之间的关系的捕获、明确的跨域图结构的建立和时间间隔信息的利用，通过域感知图卷积网络和有效的注意机制实现用户特定节点表示的学习，以及引入账户感知自注意模块进行交互特征的学习。实验结果表明，该方法在多个方面表现出优越性。