Sequential recommendation (SR) systems predict user preferences by analyzing time-ordered interaction sequences. A common challenge for SR is data sparsity, as users typically interact with only a limited number of items. While contrastive learning has been employed in previous approaches to address the challenges, these methods often adopt binary labels, missing finer patterns and overlooking detailed information in subsequent behaviors of users. Additionally, they rely on random sampling to select negatives in contrastive learning, which may not yield sufficiently hard negatives during later training stages. In this paper, we propose Future data utilization with Enduring Negatives for contrastive learning in sequential Recommendation (FENRec). Our approach aims to leverage future data with time-dependent soft labels and generate enduring hard negatives from existing data, thereby enhancing the effectiveness in tackling data sparsity. Experiment results demonstrate our state-of-the-art performance across four benchmark datasets, with an average improvement of 6.16\% across all metrics.

翻译：序列推荐系统通过分析时间有序的交互序列来预测用户偏好。该领域面临的一个普遍挑战是数据稀疏性问题，因为用户通常仅与有限数量的项目进行交互。虽然先前的研究已采用对比学习应对这一挑战，但这些方法往往采用二元标签，忽略了更精细的模式以及用户后续行为中的详细信息。此外，它们依赖随机采样来选择对比学习中的负样本，这在训练后期可能无法产生足够困难的负样本。本文提出一种用于序列推荐对比学习的未来数据利用与持久负样本生成方法。我们的方法旨在利用具有时间依赖性的软标签的未来数据，并从现有数据中生成持久的困难负样本，从而提升解决数据稀疏性问题的效能。实验结果表明，我们的方法在四个基准数据集上均取得了最先进的性能，所有指标平均提升了6.16%。