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recsys-review-process

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Use when explaining or planning around ACM RecSys peer review — the mutually-anonymous model with at least three PC members and a Senior PC overseer, the rebuttal phase, how the reproducibility-crisis culture shapes reviewer priorities, the offline-versus-online evaluation lens, and how acceptance leads to ACM Digital Library publication.

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Source SKILL.md: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/RecSys-Skills/skills/recsys-review-process/SKILL.md

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RecSys Review Process

Use this to reason about review-stage strategy. Reopen the current Call for Contributions, the committees page, and any reviewer guidelines before making process claims — mechanics are cycle-specific.

Process model

  • RecSys review is mutually anonymous (double-blind). Each submission is read by at least three PC members and overseen by a Senior PC member who synthesizes the recommendation.
  • There is an author rebuttal phase (2026: June 4-9) for a short clarifying narrative.
  • Reviewers weigh recommendation novelty, evaluation validity, reproducibility, clarity, and relevance to the recommender community — not raw metric wins alone.
  • The most useful reply is a decision-focused clarification that gives the Senior PC a clean rationale for acceptance or rejection.
  • Accepted papers are published in the ACM Digital Library, so camera-ready compliance and metadata matter as much as the initial decision.

Who reviews here, and what they distrust

  • The pool is a single-domain recommender community: expect at least one reviewer who has internalized the field's reproducibility debate and will probe baseline tuning line by line.
  • Because RecSys is topically tight, matches are close and an under-tuned comparison or a leaky split gets caught rather than skimmed past.
  • Borderline offline-evaluation papers usually fall on one of three edges: baselines tuned less hard than the proposed method, a random split where a temporal one was needed, or an offline metric asserted to imply a deployment win with no bridge.

Scoring leverage table

Review dimensionWhat raises itWhat sinks it
Recommendation noveltyA named user/item modeling or evaluation ideaAn architecture swap with no recommendation-specific insight
Evaluation validityEqual-budget baselines, temporal split, full-ranking metrics, varianceUntuned baselines, random split, sampled metrics reported as full
Deployment relevanceOff-policy estimate, simulator, or A/B result"Offline nDCG rose, therefore users benefit"
ReproducibilityA runnable anonymous repository regenerating the tablesA promise to release code "upon acceptance" only
ClarityOne notation source, honest limitationsBuried assumptions; a random-split protocol left implicit

Stage-by-stage realism

  • Initial reviews: triage by what the Senior PC would weigh, not by reviewer tone.
  • Rebuttal: the window is short; an early, precise narrative on the central evaluation objection beats a late line-by-line reply (see recsys-author-response).
  • Decision: the Senior PC synthesizes; one unresolved evaluation-validity objection outweighs several resolved clarity complaints.
  • Post-decision: ACM Digital Library publication means the rights form and metadata become the final gate.

Vignette: reading a split decision

Two reviewers like the method; one flags that baselines were tuned only on defaults. At RecSys that single objection is decisive because it maps onto the community's reproducibility anxiety — so the rebuttal must resolve that thread, with the equal-budget grid, before polishing anything the other reviewers raised.

Output format

[Current stage] submitted / reviews / rebuttal / decision / camera-ready
[Decision actors] <PC reviewers / Senior PC>
[Likely leverage] <novelty / evaluation validity / deployment relevance / reproducibility / clarity>
[Forbidden moves] <identity leak / unseen new results / unsupported deployment claims>
[Next response move] <one action>