Repeated Padding for Sequential Recommendation
<|reference_start|>Repeated Padding for Sequential Recommendation: 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}.<|reference_end|>
@article{dang2024repeated,
    title={Repeated Padding for Sequential Recommendation},
    author={Yizhou Dang, Yuting Liu, Enneng Yang, Guibing Guo, Linying Jiang,
  Xingwei Wang, Jianzhe Zhao},
    journal={arXiv preprint arXiv:2403.06372},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06372},
    primaryClass={cs.IR}
}