arXiv cs.LGPaper
Training seeds and model-selection stability in recommender-system evaluation
This is methodological hygiene work and it matters if you're publishing recommendations or building recommender systems at scale. The core finding: don't trust a single-seed result and don't assume validation rankings predict test rankings. If your eval depends on this assumption, you have unreliable conclusions. Run multiple seeds before shipping. For practitioners: add this to your checklist. For researchers: the paper gives you the framework to diagnose if your system has seed sensitivity.