Generative Recommendations via Latent Diffusion Models

Generative recommendation models commonly represent items using sequences of semantic tokens. However, these representations are typically learned independently of the recommendation task, potentially discarding information relevant to next-item predictions. We introduce a latent diffusion framework that jointly learns item representations from both content and interaction history and a masked denoising model for next-item recommendation. Our variational formulation supports discrete, continuous, and hybrid latent variables while encouraging discriminative representations and reducing collisions. Using a straight-through estimator, we allow the denoiser to update the item encoder, while our catalog-retrieval objective reduces collisions. We additionally propose beam-free retrieval based on denoising scores. In our experiments on three Amazon Reviews datasets, our method outperforms comparable two-stage methods. Further, our ablations demonstrate that joint training improves recommendation performance and reduces collisions.