  Sequential recommendation aims to provide users with personalized suggestions
based on their historical interactions. When training sequential models,
padding is a widely adopted technique for two main reasons: 1) The vast
majority of models can only handle fixed-length sequences; 2) Batching-based
training needs to ensure that the sequences in each batch have the same length.
The special value \emph{0} is usually used as the padding content, which does
not contain the actual information and is ignored in the model calculations.
This common-sense padding strategy leads us to a problem that has never been
explored before: \emph{Can we fully utilize this idle input space by padding
other content to further improve model performance and training efficiency?}
  In this paper, we propose a simple yet effective padding method called
\textbf{Rep}eated \textbf{Pad}ding (\textbf{RepPad}). Specifically, we use the
original interaction sequences as the padding content and fill it to the
padding positions during model training. This operation can be performed a
finite number of times or repeated until the input sequences' length reaches
the maximum limit. Our RepPad can be viewed as a sequence-level data
augmentation strategy. Unlike most existing works, our method contains no
trainable parameters or hyperparameters and is a plug-and-play data
augmentation operation. Extensive experiments on various categories of
sequential models and five real-world datasets demonstrate the effectiveness
and efficiency of our approach. The average recommendation performance
improvement is up to 60.3\% on GRU4Rec and 24.3\% on SASRec. We also provide
in-depth analysis and explanation of what makes RepPad effective from multiple
perspectives. Our datasets and codes are available at
\url{https://github.com/KingGugu/RepPad}.
