recsys-related-work
GitHub辅助ACM RecSys论文定位，检查与SIGIR、KDD等邻域文献及可复现性批判线的关联。确保双盲合规、避免重复提交，并验证对推荐子领域的引用覆盖度以通过新颖性审查。
Trigger Scenarios
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill recsys-related-work -g -y
SKILL.md
Frontmatter
{
"name": "recsys-related-work",
"description": "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 reviewers expect across recommendation subfields."
}
RecSys Related Work
Use this to audit novelty and eligibility. Reopen the current Call for Contributions for dual-submission, anonymity, and prior-publication rules before advising authors.
Positioning checks
- Separate the recommendation contribution from generic ML improvement: a new ranking objective, user/item modeling idea, evaluation protocol, off-policy estimator, or empirical insight about deployed behavior.
- Cover the recommendation subfields your claim touches: collaborative filtering, sequential/ session models, off-policy/counterfactual evaluation, fairness/diversity/exposure, and — for any empirical claim — the reproducibility-critique line.
- Treat ACM Digital Library, journal, and formal conference proceedings as archival unless current rules say otherwise.
- Cite arXiv and prior-version work so double-blind review survives; do not point reviewers to identity-revealing pages.
- Explain overlap with any concurrent or prior version, and do not submit duplicate archival work.
Neighbor-venue coverage table
RecSys is a single-domain venue, but its neighbors publish recommendation-relevant work. A reviewer checks whether you cite the right neighbor, not just RecSys itself.
| Neighbor venue | What it contributes to your related work | Reviewer check |
|---|---|---|
| SIGIR | Ranking models, IR evaluation, neural retrieval | Did you distinguish recommendation from ad-hoc retrieval framing? |
| KDD | Large-scale mining, the sampled-metrics critique | Is the scalability or evaluation-methodology neighbor acknowledged? |
| WSDM / TheWebConf | Web-scale recommendation, graph and CF methods | Is the nearest web-recommendation method compared? |
| UAI | Foundational ranking (e.g., BPR) and probabilistic modeling | Are canonical recommendation methods cited to their true venue, not RecSys? |
| Reproducibility line | "Are we really making much progress?" and follow-ups | Does your evaluation answer the tuned-baseline critique head on? |
A bibliography that cites only RecSys papers tells a reviewer you may have missed the method that already solved this at a neighbor venue — a recognizable reject pattern that benchmark strength does not repair.
Positioning vignette
Imagine the paper proposes an exposure-corrected session ranker. Its nearest neighbors: a SIGIR neural ranker with no exposure correction, a KDD paper on sampled-metric bias, and a prior RecSys counterfactual-embedding paper. The novelty sentence should name all three contrasts — exposure handling where the SIGIR line ignored it, full-ranking evaluation answering the KDD critique, and a session-level objective where the prior RecSys work was static.
Concurrent-work judgment calls
- Independently concurrent arXiv work: cite neutrally, state the technical difference, avoid priority claims reviewers cannot verify.
- Your own prior version: verify its archival status against the current CFP and phrase the citation so double-blind review survives.
- When unsure whether a venue is archival, declare the overlap on the submission form rather than gambling on a chair's interpretation.
Output format
[Eligibility] clear / needs declaration / risky
[Closest subfields] <CF / sequential / off-policy / fairness / reproducibility>
[Nearest 3 works] <work -> distinction (with true venue)>
[Archival-overlap risk] <none / issues>
[Novelty sentence] <RecSys-ready recommendation contribution contrast>
Version History
- 9f86f09 Current 2026-07-19 17:28
