English(EN) Training seeds and model-selection stability in recommender-system evaluation

研究强调训练种子对推荐系统评估的影响

作者 PulseAugur 编辑部 · [2 个来源] · 2026-09-02 12:06

一篇新发表在arXiv上的研究论文探讨了随机训练种子对推荐系统评估的影响。研究发现，改变训练种子会显著影响评估结论，进而影响参数初始化、数据采样和模型选择。研究结果表明，依赖单一种子可能会夸大推荐系统评估的稳定性，并且训练种子应被视为评估协议的关键组成部分。 AI

影响强调了推荐系统中需要更严格的评估协议，这可能会影响AI模型的基准测试方式。

排序理由该集群包含一篇在arXiv上发表的详细介绍实验结果的研究论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv cs.LG TIER_1 English(EN) · Juan Manuel Rodriguez, Oleg Lesota, Antonela Tommasel · 2026-09-03 04:00

训练种子与推荐系统评估中的模型选择稳定性

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…
arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Antonela Tommasel · 2026-09-02 12:06

训练种子与模型选择稳定性在推荐系统评估中的应用

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 …