Sequential recommendation methods play a pivotal role in modern recommendation systems. A key challenge lies in accurately modeling user preferences in the face of data sparsity. To tackle this challenge, recent methods leverage contrastive learning (CL) to derive self-supervision signals by maximizing the mutual information of two augmented views of the original user behavior sequence. Despite their effectiveness, CL-based methods encounter a limitation in fully exploiting self-supervision signals for users with limited behavior data, as users with extensive behaviors naturally offer more information. To address this problem, we introduce a novel learning paradigm, named Online Self-Supervised Self-distillation for Sequential Recommendation ($S^4$Rec), effectively bridging the gap between self-supervised learning and self-distillation methods. Specifically, we employ online clustering to proficiently group users by their distinct latent intents. Additionally, an adversarial learning strategy is utilized to ensure that the clustering procedure is not affected by the behavior length factor. Subsequently, we employ self-distillation to facilitate the transfer of knowledge from users with extensive behaviors (teachers) to users with limited behaviors (students). Experiments conducted on four real-world datasets validate the effectiveness of the proposed method.

翻译：序列推荐方法在现代推荐系统中扮演着关键角色。其核心挑战在于面对数据稀疏性时如何准确建模用户偏好。为解决这一难题，现有方法通过最大化原始用户行为序列两个增广视图之间的互信息，利用对比学习获取自监督信号。尽管这些方法表现有效，但基于对比学习的方法在完全利用行为数据有限用户的自监督信号方面存在局限——行为数据丰富的用户天然能提供更多信息。针对该问题，我们提出一种名为"S^4Rec"（面向序列推荐的在线自监督自蒸馏方法）的新型学习范式，有效弥合了自监督学习与自蒸馏方法之间的鸿沟。具体而言，我们通过在线聚类高效地将用户按其潜在意图进行分组，并采用对抗学习策略确保聚类过程不受行为长度因素影响。随后利用自蒸馏方法将知识从行为丰富的用户（教师）迁移至行为有限的用户（学生）。在四个真实数据集上的实验结果验证了所提方法的有效性。