agentsclimarketplace

Recsys writing style

Skill brycewang-stanford/Awesome-Journal-Skills/RecSys-Skills/skills/recsys-writing-style

Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. | 覆盖主流期刊的 Claude Code/Codex 期刊技能包,从选题、识别策略到表格规范与审稿回复全流程,助你快速发论文。

Install
npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill recsys-writing-style

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

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 supports rather than to leaderboard language.

SKILL.md

3.8 KB, 788 tokens by cl100k_base, as published. Nobody here has run it

RecSys Writing Style

Use this when revising the main paper. RecSys papers need a compact statement of why a recommendation result matters and enough evaluation detail to survive a reproducibility-minded reviewer.

Revision rules

  • Put the recommendation contribution on the first page: problem, gap, method, offline evidence, and the offline-to-deployment bridge.
  • Make the evaluation protocol explicit early: the split (temporal vs random), whether metrics are full-ranking or sampled, and that baselines are tuned under an equal budget.
  • Pair every empirical claim with a table, an ablation, or an off-policy/A-B result — not with a superlative.
  • Use the 8-page body for core logic; move tuning grids, extra datasets, and per-dataset breakdowns to the appendix (which counts inside the budget) without making the body unreadable.
  • Avoid leaderboard framing ("state of the art," "outperforms all baselines") when the gain is small, the variance unreported, or the baselines under-tuned.
  • Maintain double-blind style in self-citations, platform names, acknowledgements, funding, and the repository description.

Claim-discipline for recommender papers

  • State the split protocol in words, not just in a config: "we use a temporal leave-one-last split" pre-empts the leakage objection.
  • Say the baselines were tuned with the same budget as the method; this one sentence defuses the field's central reproducibility complaint.
  • When an offline metric is the only evidence, scope the claim to offline; do not let "nDCG rose" masquerade as "users are better served."
  • Report variance (mean ± sd over seeds), and in captions say whether bars are sd, confidence intervals, or quantiles.
  • Label beyond-accuracy goals (diversity, fairness, exposure) as measured quantities, not asserted virtues.

Sentence-level rewrites

Draft patternRecSys-safe rewrite
"Our model significantly outperforms all baselines.""improves nDCG@20 by X (sd Y) over equal-budget-tuned baselines"
"We evaluate on a standard split.""We use a temporal leave-one-last split to avoid future leakage."
"Achieves state-of-the-art recommendation."Claim scoped to the datasets and cutoff actually tested
"Users will benefit from better recommendations.""our off-policy estimate of engagement rises; deployment is future work"
"We use Recall@20.""Recall@20 over the full item catalog (not a sampled candidate set)"

Vignette: compressing into eight two-column pages

A draft with a model, five datasets, and a sprawling related-work section: keep the method, the assumption, the headline tuned table, one mechanism ablation, and the offline-online bridge in the body; compress related work into contribution contrasts; move the tuning grid, two datasets, and per-dataset breakdowns to the appendix with explicit forward references. The test of a good cut: a reviewer should reconstruct the whole argument, including how the baselines were tuned, without opening the repository.

Output format

[Writing diagnosis] clear / under-justified / overclaimed / leakage-ambiguous
[First-page fix] <new recommendation-first framing>
[Claim discipline] <claim -> table / ablation / off-policy result / scoped limitation>
[Evaluation wording] <split protocol / metric type / tuning-budget statement>
[Compression cuts] <move / delete / merge>
[Anonymity edits] <phrases to rewrite>

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.