arXiv (OAI Expanded)
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Training seeds and model-selection stability in recommender-system evaluation
Recommender-system experiments often rely on a single random training seed, assuming that run-to-run stochasticity has limited impact on evaluation conclusions. This assumption is risky, as a training seed may influence several algorithm-dependent mechanisms, including parameter initialization, mini
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