A new interests burn-down diffusion process models decaying user interests for personalized collaborative filtering and outperforms prior generative methods in the StageCF implementation.
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EchoAlign adjusts instances with controllable generative models to match noisy labels and selects reliable subsets, outperforming prior methods on benchmarks especially under 30% instance-dependent noise.
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Interests Burn-down Diffusion Process for Personalized Collaborative Filtering
A new interests burn-down diffusion process models decaying user interests for personalized collaborative filtering and outperforms prior generative methods in the StageCF implementation.
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EchoAlign: Bridging Generative and Discriminative Learning under Noisy Labels
EchoAlign adjusts instances with controllable generative models to match noisy labels and selects reliable subsets, outperforming prior methods on benchmarks especially under 30% instance-dependent noise.