  Self-supervised contrastive learning, which directly extracts inherent data
correlations from unlabeled data, has been widely utilized to mitigate the data
sparsity issue in sequential recommendation. The majority of existing methods
create different augmented views of the same user sequence via random
augmentation, and subsequently minimize their distance in the embedding space
to enhance the quality of user representations. However, random augmentation
often disrupts the semantic information and interest evolution pattern inherent
in the user sequence, leading to the generation of semantically distinct
augmented views. Promoting similarity of these semantically diverse augmented
sequences can render the learned user representations insensitive to variations
in user preferences and interest evolution, contradicting the core learning
objectives of sequential recommendation. To address this issue, we leverage the
inherent characteristics of sequential recommendation and propose the use of
context information to generate more reasonable augmented positive samples.
Specifically, we introduce a context-aware diffusion-based contrastive learning
method for sequential recommendation. Given a user sequence, our method selects
certain positions and employs a context-aware diffusion model to generate
alternative items for these positions with the guidance of context information.
These generated items then replace the corresponding original items, creating a
semantically consistent augmented view of the original sequence. Additionally,
to maintain representation cohesion, item embeddings are shared between the
diffusion model and the recommendation model, and the entire framework is
trained in an end-to-end manner. Extensive experiments on five benchmark
datasets demonstrate the superiority of our proposed method.
