Context-Aware Diffusion-based Sequential Recommendation

Sequential recommendation aims to recommend the next item that matches a user’s interest, based on the sequence of items he/she interacted with before. Although effective, existing work suffers from the following limitations: (1) Existing diffusion-based recommendation methods have undertaken tailored refinements to the diffusion process without considering the difference between recommendation and other tasks, leading to the ignorance of the user’s personalized preferences; (2) Self-supervised contrastive learning, widely used to mitigate the data sparsity issue in sequential recommendation, typically employs random augmentation to create multiple views of user sequences. However, random augmentation can disrupt the semantic integrity and interest patterns within the sequence, resulting in semantically divergent augmented views that may misrepresent user preferences. To address these challenges, we propose the Context-Aware Diffusion-based Sequential Recommendation (CADSR) model, which leverages context information to generate more semantically consistent positive samples during contrastive learning. This ensures that the model captures both user preferences and their evolution more accurately. Extensive experiments on four public benchmark datasets show that CADSR outperforms 11 state-of-the-art baselines, achieving an average improvement of 10.94% in Recall@10 and 10.54% in NDCG@10 over the best baseline. Source code is available at https://github.com/queenjocey/CADSR.