Individual skill
recsys-artifact-evaluation
Use when packaging ACM RecSys code, datasets, splits, trained models, propensity logs, and seeds as an anonymous in-paper repository during review or a public archive after acceptance, even though RecSys has no separate artifact badge — covering what recommender reviewers open first and how to make a top-N ranking table regenerable end to end.
RecSys-Skills/skills/recsys-artifact-evaluation/SKILL.md
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# RecSys Artifact Evaluation
Use this for evidence packaging around RecSys. The venue does not run a separate artifact-badge
process; instead the Call for Contributions expects a link to an **anonymous repository** inside
the paper, and reproducibility-minded reviewers judge the paper partly by whether that repository
makes the ranking claims regenerable.
## Artifact plan
- Decide what a reviewer needs to trust the claim: the exact dataset version, the split script,
baseline configurations, tuning grids, trained-model checkpoints, exposure/propensity logs for
off-policy claims, seeds, and the evaluation code.
- Keep decision-critical evidence in the paper or appendix; optional run files live in the
repository, because RecSys reviewers are not obliged to open it.