用未来数据和硬负例提升序列推荐，解决用户行为稀疏问题。
Future Sight and Tough Fights: Revolutionizing Sequential Recommendation with FENRec
- 用时间相关的软标签利用未来数据增强学习
- 从已有数据生成持久硬负例，提升对比学习效果
- 在四个数据集上平均性能提升6.16%，适合推荐系统研究者
序列推荐（SR）系统通过分析用户交互的时间顺序序列来预测偏好。常见挑战是数据稀疏，用户通常仅与少量项目互动。尽管已有方法采用对比学习应对该问题，但多使用二元标签，忽略后续行为中的细微模式和详细信息。此外，随机采样负样本的方法在训练后期可能无法生成足够困难的负例。本文提出一种名为FENRec的框架，通过利用未来数据并采用时间依赖的软标签，同时从现有数据中生成持久的硬负例，以增强对数据稀疏性的建模能力。实验结果表明，在四个基准数据集上，FENRec实现最先进性能，所有指标平均提升6.16%。
原文摘要 · Abstract (English)
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.
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