Sequential recommendation effectively models dynamic user interests but continues to face challenges related to data sparsity. While self-supervised learning has alleviated this issue to some extent, most existing methods focus exclusively on immediate next-item prediction during training, thereby neglecting the rich information embedded in longer-term future interactions. Although a few studies have explored the utilization of future data, existing attempts typically apply future supervision signals with uniform intensity across all samples, which may lead to suboptimal solutions. In this paper, we propose an adaptive future learning framework, UFRec, which encourages the model to look further ahead when it is confident in the current state, while focusing on the immediate task when it is uncertain. Specifically, UFRec incorporates an Uncertainty-Guided Future Supervision module that dynamically modulates the weight of multi-step future supervision based on the model's confidence in the primary next-item prediction task. Furthermore, we complement step-wise future supervision with a Future-Aware Contrastive Learning module that treats the future trajectory as a holistic entity. Notably, both auxiliary modules are utilized exclusively during training and incur no inference overhead. Extensive experiments on four benchmark datasets demonstrate that our method significantly outperforms state-of-the-art approaches by effectively leveraging future data.

翻译：序列推荐能够有效建模动态用户兴趣，但持续面临数据稀疏性的挑战。尽管自监督学习在一定程度上缓解了该问题，但现有方法在训练时大多仅关注于下一项的即时预测，从而忽略了长期未来交互中蕴含的丰富信息。尽管已有少数研究探索了未来数据的利用，但现有尝试通常对所有样本施加统一强度的未来监督信号，可能导致次优解。本文提出自适应未来学习框架UFRec，其鼓励模型在自信当前状态时展望更远的未来，而在不确定时聚焦即时任务。具体而言，UFRec包含不确定性引导的未来监督模块，该模块基于模型在主任务（下一项预测）中的置信度动态调节多步未来监督的权重。此外，我们通过未来感知对比学习模块对逐步未来监督进行补充，将未来轨迹视为整体实体。值得注意的是，两个辅助模块仅在训练阶段使用，且不增加推理开销。在四个基准数据集上的广泛实验表明，本方法通过有效利用未来数据，显著优于现有最先进方法。