recsys-skills

Plugin recsys-skills from marketplace awesome-journal-skills in brycewang-stanford/Awesome-Journal-Skills. Commands read from the repo's manifest — 1 month ago.

Skills (12)

• 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

• recsys-author-response

  Use when drafting an ACM RecSys rebuttal during the author-response window, covering the short narrative RecSys allows, anonymity, anchoring answers to submitted evidence and the anonymous repository, the recurring objections about untuned baselines, leakage, and offline-online gaps, and Senior-PC-f

• recsys-camera-ready

  Use when preparing an accepted ACM RecSys paper for camera-ready, covering ACM two-column de-anonymization and reflow, the ACM Digital Library rights/copyright form, metadata and DOI, integrating rebuttal-promised fixes without overreach, releasing the previously-anonymous code/data repository publi

• 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

• recsys-related-work

  Use when positioning an ACM RecSys submission against recommender-systems literature and its neighbors (SIGIR, KDD, WSDM, TheWebConf, UAI), including the reproducibility-critique line, arXiv and prior versions, concurrent work, ACM Digital Library archival status, and the citation coverage RecSys re

• recsys-reproducibility

  Use when strengthening the reproducibility of an ACM RecSys paper or preparing a RecSys Reproducibility Track submission — pinning dataset versions and splits, tuning baselines under an equal budget, reporting seeds and variance, avoiding sampled-metric distortion, and structuring a reproduction stu

• recsys-review-process

  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 l

• recsys-submission

  Use when auditing an ACM RecSys submission before the deadline — routing between the Main, Reproducibility, Industry and Resource tracks, the ACM two-column content-page budgets with appendices counted inside, double-blind anonymization for recommender papers, the anonymous-repository expectation, a

• recsys-supplementary

  Use when organizing ACM RecSys supporting material under the venue's no-separate-supplement convention — how to split a recommender paper between the body, the appendix that counts inside the page budget, and the anonymous in-paper repository, so tuning grids, extra datasets, and ablations land wher

• recsys-topic-selection

  Use when deciding whether a project is a strong ACM RecSys fit, routing among RecSys, SIGIR, KDD, WSDM, TheWebConf, UAI, CHI, and general ML venues, identifying whether the core contribution is genuinely about recommendation, and choosing the right RecSys track before writing begins.

• recsys-workflow

  Use when planning an ACM RecSys project timeline from venue fit through abstract, full paper, the anonymous repository, rebuttal, decision, ACM camera-ready, registration, and presentation, with backward-planning offsets for an offline-evaluation recommender paper and per-track date awareness (Main,

• recsys-writing-style

  Use when revising an ACM RecSys paper for a recommendation-first first page, honest offline-versus-online framing, equal-budget baseline claims, leakage-aware evaluation wording, ACM two-column 8-page compression, double-blind phrasing, and claims scoped to what the ranking evidence actually support

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