When Diffusion Recommenders Stop Sampling: Preserving Generative Diversity

A user history can admit multiple plausible next interactions, yet conventional sequential recommenders produce a single deterministic ranking. While diffu- sion models offer a natural way to sample alternative continuations, we show that existing target-item diffusion recommenders often suffer from severe sampling collapse, producing nearly identical outputs across independent draws. We iden- tify two ubiquitous design choices that promote this collapse: joint learning of the latent space and denoiser, and in-trajectory supervision via discrete losses. To address these failure modes, we introduce DARTS (Diverse and Accurate Recommendation Through Sampling), a three-stage latent diffusion framework that explicitly decouples representation learning, generative modeling, and dis- crete ranking by learning and freezing the latent space before diffusion training and postponing ranking supervision to the final decoding stage. We further char- acterize sampling diversity using complementary measures of variation before and after decoding, and use posterior-predictive aggregation to exploit this diversity at inference. Across three Amazon Reviews 2023 categories, DARTS preserves substantially greater sampling diversity while outperforming strong sequential, generative-retrieval, and diffusion baselines by 7–12% on standard ranking met- rics. Aggregating multiple samples further improves ranking accuracy at a fixed slate size.