Sub-sequence splitting (SSS) has been demonstrated as an effective approach to mitigate data sparsity in sequential recommendation (SR) by splitting a raw user interaction sequence into multiple sub-sequences. Previous studies have demonstrated its ability to enhance the performance of SR models significantly. However, in this work, we discover that \textbf{(i). SSS may interfere with the evaluation of the model's actual performance.} We observed that many recent state-of-the-art SR models employ SSS during the data reading stage (not mentioned in the papers). When we removed this operation, performance significantly declined, even falling below that of earlier classical SR models. The varying improvements achieved by SSS and different splitting methods across different models prompt us to analyze further when SSS proves effective. We find that \textbf{(ii). SSS demonstrates strong capabilities only when specific splitting methods, target strategies, and loss functions are used together.} Inappropriate combinations may even harm performance. Furthermore, we analyze why sub-sequence splitting yields such remarkable performance gains and find that \textbf{(iii). it evens out the distribution of training data while increasing the likelihood that different items are targeted.} Finally, we provide suggestions for overcoming SSS interference, along with a discussion on data augmentation methods and future directions. We hope this work will prompt the broader community to re-examine the impact of data splitting on SR and promote fairer, more rigorous model evaluation. All analysis code and data will be made available upon acceptance. We provide a simple, anonymous implementation at https://github.com/KingGugu/SSS4SR.

翻译：子序列拆分（SSS）已被证明是一种有效缓解序列推荐（SR）中数据稀疏性的方法，通过将原始用户交互序列拆分为多个子序列。先前研究已证明其能显著提升SR模型的性能。然而，在本工作中，我们发现\textbf{(i) SSS可能干扰模型实际性能的评估。}我们观察到，近期许多最先进的SR模型在数据读取阶段使用了SSS（论文中未提及）。当移除该操作后，模型性能显著下降，甚至低于早期经典SR模型。SSS及不同拆分方法在不同模型上带来的性能提升差异，促使我们进一步分析SSS在何时有效。我们发现\textbf{(ii) 只有当特定拆分方法、目标策略和损失函数联合使用时，SSS才展现出强大能力。}不恰当的组合甚至可能损害性能。此外，我们分析了子序列拆分为何能带来如此显著的性能提升，发现\textbf{(iii) 它均衡了训练数据的分布，同时增加了不同物品被作为目标的可能性。}最后，我们提出了克服SSS干扰的建議，并讨论了数据增强方法与未来方向。希望本工作能促使更广泛的研究社区重新审视数据拆分对SR的影响，推动更公平、更严格的模型评估。所有分析代码和数据将在论文被接收后公开。我们提供简化的匿名实现，链接为https://github.com/KingGugu/SSS4SR。