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Research highlights impact of training seeds on recommender system evaluation

By PulseAugur Editorial · [2 sources] ·

A new research paper published on arXiv explores the impact of random training seeds on the evaluation of recommender systems. The study found that varying the training seed can significantly affect evaluation conclusions, influencing parameter initialization, data sampling, and model selection. The findings suggest that relying on a single seed may overstate the stability of recommender system evaluations and that training seeds should be considered a crucial part of the evaluation protocol. AI

IMPACT Highlights the need for more rigorous evaluation protocols in recommender systems, potentially impacting how AI models are benchmarked.

RANK_REASON The cluster contains a research paper published on arXiv detailing experimental findings.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Research highlights impact of training seeds on recommender system evaluation

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Juan Manuel Rodriguez, Oleg Lesota, Antonela Tommasel ·

    Training seeds and model-selection stability in recommender-system evaluation

    arXiv:2609.02499v1 Announce Type: cross Abstract: 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 al…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Antonela Tommasel ·

    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 …

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