DADF adds a plug-in second-stage debiasing layer with dynamic target transformation, duration-aware residual modeling, and multi-label auxiliary signals to reduce local calibration errors in long-tailed watch-time regression.
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Foundation models slightly outperform task-specific models on probabilistic electricity price forecasts but the gap narrows or reverses with extra features or few-shot adaptation, showing that efficiency often outweighs marginal accuracy gains.
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DADF: A Distribution-Aware Debiasing Framework for Watch-Time Regression in Recommender Systems
DADF adds a plug-in second-stage debiasing layer with dynamic target transformation, duration-aware residual modeling, and multi-label auxiliary signals to reduce local calibration errors in long-tailed watch-time regression.
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Assessing the Performance-Efficiency Trade-off of Foundation Models in Probabilistic Electricity Price Forecasting
Foundation models slightly outperform task-specific models on probabilistic electricity price forecasts but the gap narrows or reverses with extra features or few-shot adaptation, showing that efficiency often outweighs marginal accuracy gains.