Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation

arXiv:2404.07219v1 Announce Type: cross
Abstract: 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 …

arxiv cs.ir cs.lg distillation recommendation type