Why is Repeated Padding Effective for Sequential Recommendation

doi:10.1109/tkde.2026.3715453

Why is Repeated Padding Effective for Sequential Recommendation

2026-07-20

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DOI：10.1109/tkde.2026.3715453

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Padding is a standard technique for training sequential recommendation models. It fills the short sequences to the same length so they can be fed into the model in batches. Traditional zero-padding uses the special value 0 as the padding content. However, zero-padding results in a significant waste of input space due to widespread data sparsity and the fact that the special value 0 is not involved in the model calculations. To tackle that, researchers propose repeated padding (RepPad) Dang et al. 2024, which uses the original sequence as the padding content during training. Although this method turns out to be effective on many different types of sequential recommendation models, what underlies the performance gains is still a mystery. In this paper, we first review the operations of RepPad for input, positive, and negative sequences. Based on the operations, we decompose RepPad into twelve variants ranging from zero-padding to full RepPad. Through an empirical study, we disclose that RepPad’s effectiveness derives from performing repeated padding on input, positive sequences, and the extended random negative sequence. These three operations must exist simultaneously. Furthermore, we derive and analyze the twelve variants step-by-step from a loss function perspective, which helps clarify the changes brought about by RepPad compared to zero-padding during loss calculations. Based on these analyses, we refine the previous three operations into three reasons why RepPad is effective: maintaining sequence alignment and causality, increasing information density and diversity, and generating more negative samples in the single propagation. Based on these findings, we put forward the Repeated Padding with Extended Negative Sequence (RepPad-ENS) for sequential recommendation, which samples multiple extended random negative sequences based on RepPad. It unlocks the potential of RepPad by improving the quantity and diversity of negative samples in the single propagation, thereby facilitating more accurate preference learning. Comprehensive experiments on various categories of baselines with real-world datasets demonstrate the effectiveness, efficiency, and generalizability of our method.

Keywords:

Recommender systems

sequential recommendation

padding

data augmentation

Why is Repeated Padding Effective for Sequential Recommendation

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