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Training seeds can dramatically skew recommender system evaluations, revealing that relying on a single seed may misrepresent model performance.
Users can tell the difference in popularity composition of music recommendations, but they don鈥檛 necessarily prefer the calibrated options.
Autoguidance鈥攗sing a model to guide itself鈥攃an effectively reduce popularity bias in diffusion recommenders, leading to fairer item exposure without significantly sacrificing accuracy.
