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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 diffusion models offer a natural way to sample alternative continuations, we show that existing target-item diffusion recommenders often suffer from s
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