技能 人工智能 推荐系统相关工作定位指南

推荐系统相关工作定位指南

v20260724
recsys-related-work
这是一份为顶级推荐系统会议(RecSys)设计的相关工作撰写指南。它指导作者如何定位论文的创新点,确保贡献与邻近领域的既有研究(如SIGIR、KDD)拉开距离。核心在于审核论文的独特性、评估方法(如Off-policy)以及与前人工作的差异,以提升论文的学术可接受性。
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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>
信息
Category 人工智能
Name recsys-related-work
版本 v20260724
大小 3.79KB
更新时间 2026-07-29
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