Conditional Denoising Diffusion for Sequential Recommendation

Contemporary attention-based sequential recommendations often encounter the oversmoothing problem, which generates indistinguishable representations. Although contrastive learning addresses this problem to a degree by actively pushing items apart, we still identify a new ranking plateau issue. This issue manifests as the ranking scores of top retrieved items being too similar, making it challenging for the model to distinguish the most preferred items from such candidates. This leads to a decline in performance, particularly in top-1 metrics. In response to these issues, we present a conditional denoising diffusion model that includes a stepwise diffuser, a sequence encoder, and a cross-attentive conditional denoising decoder. This approach streamlines the optimization and generation process by dividing it into simpler, more tractable sub-steps in a conditional autoregressive manner. Furthermore, we introduce a novel optimization scheme that incorporates both cross-divergence loss and contrastive loss. This new training scheme enables the model to generate high-quality sequence/item representations while preventing representation collapse. We conduct comprehensive experiments on four benchmark datasets, and the superior performance achieved by our model attests to its efficacy. We open-source our code at https://github.com/YuWang-1024/CDDRec.