  Sequential recommender systems have recently achieved significant performance
improvements with the exploitation of deep learning (DL) based methods.
However, although various DL-based methods have been introduced, most of them
only focus on the transformations of network structure, neglecting the
importance of other influential factors including data augmentation. Obviously,
DL-based models require a large amount of training data in order to estimate
parameters well and achieve high performances, which leads to the early efforts
to increase the training data through data augmentation in computer vision and
speech domains. In this paper, we seek to figure out that various data
augmentation strategies can improve the performance of sequential recommender
systems, especially when the training dataset is not large enough. To this end,
we propose a simple set of data augmentation strategies, all of which transform
original item sequences in the way of direct corruption and describe how data
augmentation changes the performance. Extensive experiments on the latest
DL-based model show that applying data augmentation can help the model
generalize better, and it can be significantly effective to boost model
performances especially when the amount of training data is small. Furthermore,
it is shown that our proposed strategies can improve performances to a better
or competitive level to existing strategies suggested in the prior works.
