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Research highlights risks of single-seed evaluation in recommender systems

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 using a single random seed can lead to unreliable conclusions, as it influences various algorithm-dependent mechanisms. Varying the training seed across different hyperparameter configurations revealed that seed variation can be significant, affecting user-level metrics, model selection, and the similarity of recommendation lists. The findings suggest that single-seed results may overstate the stability of recommender system evaluations and that training seeds should be considered a formal part of the evaluation protocol. AI

IMPACT Highlights potential instability in recommender system evaluations, urging researchers to adopt more robust methodologies.

RANK_REASON The cluster contains a research paper published on arXiv detailing findings about recommender system evaluation. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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

Research highlights risks of single-seed evaluation in recommender systems

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The cluster contains a research paper published on arXiv detailing findings about recommender system evaluation. [lever_c_demoted from research: ic=1 ai=0.7]
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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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