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recsys-experiments
Use when designing or auditing ACM RecSys experiments centered on offline-versus-online evaluation — temporal splits, equal-budget baseline tuning, full-ranking versus sampled metrics, off-policy estimators (IPS, SNIPS, doubly robust), A/B tests, exposure and popularity bias, seeds and variance, and matching each recommendation claim to its evidence.
RecSys-Skills/skills/recsys-experiments/SKILL.md
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# RecSys Experiments
Use this before submission when the empirical story is not yet locked. At RecSys the experiment
section is where papers are won or lost, because the community's reproducibility culture makes
reviewers read evaluation choices as a proxy for whether the gains are real.
## Experiment audit
- Map each recommendation claim to a table, figure, ablation, off-policy estimate, or A/B result.
- Tune every baseline under an **equal search budget**; an under-tuned baseline is the fastest
route to a low score at this venue.
- Use a **leakage-aware split**: temporal or leave-one-last for sequential/session data, never a
random split that lets future interactions into training.
- Rank over the **full item catalog** or state clearly that a sampled candidate set was used and