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name: recsys-artifact-evaluation description: 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 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.
• Anonymize repository history, commit authors, cluster paths, license headers, and any platform or organization names.
• Include a one-minute reproduction map: environment, dependencies, dataset download or identifier, commands, expected ranking numbers, runtime, seeds, and known nondeterminism.
• For proprietary interaction data, give enough provenance and preprocessing detail for credible reproduction on a public dataset without violating data-use terms.
• After acceptance, swap the anonymous mirror for a public, licensed, citable archive.

What RecSys evidence reviewers open first

Claim type	First artifact inspected	Common failure caught
Top-N ranking gain	Split script + baseline configs	Random split leaking the future; baselines under-tuned
Off-policy / counterfactual result	Logged propensities + estimator code	Propensities missing, so the IPS/DR estimate cannot be recomputed
Sequential/session model	Data ordering + leave-one-last split	Test interactions seen during training
Reported metric values	The scorer and its cutoff	Sampled metrics presented as full-ranking numbers

Because RecSys reviewers can and do re-run a small offline pipeline, make the headline ranking table regenerable with one command before polishing anything else.

Worked vignette: packaging an off-policy study

A hypothetical submission proposes an exposure-corrected ranker validated offline and in a semi-synthetic simulator.

• Ship the logging policy's propensities alongside the interactions, not just the clicks, so the inverse-propensity estimate can be recomputed.
• Provide the simulator as one parameterized script so a reviewer can vary exposure strength and reward, rather than trusting a single frozen curve.
• Emit every ranking and reward table directly from logged results so the PDF numbers and the repository numbers cannot drift apart.
• State which regime the simulator satisfies the positivity assumption in and where it breaks it, since that mapping is what reproducibility-minded reviewers grade.

Calibration anchors

repo/
  README.md            # one-minute orientation: env, data id, one command per table
  environment.yml      # pinned versions of the recommender framework and deps
  data/PREP.md         # dataset version, split protocol (temporal), checksums
  configs/             # per-model + per-baseline configs with tuning grids
  run_tables.sh        # regenerates Table 1..N from seeds

• Assume only the README and one entry script get opened; design for that.
• Repository size limits and accepted hosting change by cycle; verify against the current submission instructions rather than a past year.

Output format

[Artifact role] anonymous in-paper repo / camera-ready public archive
[Contents] <data/splits/configs/checkpoints/propensities/seeds>
[Anonymity risks] <paths / commit authors / platform names / URLs>
[Reproduction level] turnkey / scripted / descriptive / weak
[Fixes before upload] <ordered list>