Diffusion-based Contrastive Learning for Sequential Recommendation

Understand

Contrastive learning has been effectively utilized to enhance the training of sequential recommendation models by leveraging informative self-supervised signals.

• Most existing approaches generate augmented views of the same user sequence through random augmentation and subsequently maximize their agreement in the representation space.
• However, these methods often neglect the rationality of the augmented samples.
• Due to significant uncertainty, random augmentation can disrupt the semantic information and interest evolution patterns inherent in the original user sequences.