Repeated Padding+: Simple yet Effective Data Augmentation Plugin for Sequential Recommendation
Yizhou Dang, Yuting Liu, Enneng Yang, Guibing Guo, Linying Jiang, Jianzhe Zhao, Xingwei Wang

TL;DR
Rearranged padding with RepPad+ enhances sequential recommendation models by utilizing original sequences as padding, significantly improving performance without additional training parameters across multiple datasets.
Contribution
Introduces RepPad+, a simple, parameter-free data augmentation padding method that reuses original sequences to improve sequential recommendation models.
Findings
Up to 84.11% performance improvement on GRU4Rec
Up to 35.34% performance improvement on SASRec
Effective across various models and datasets
Abstract
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: Can we fully utilize this idle input space by padding other content to further improve model performance and training efficiency? In this work, we propose a simple yet effective padding method called Repeated Padding+ (RepPad+). Specifically, we use the original…
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Taxonomy
TopicsRecommender Systems and Techniques · Customer churn and segmentation
