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recsys-related-work

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 reviewers expect across recommendation subfields.

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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 venueWhat it contributes to your related workReviewer check
SIGIRRanking models, IR evaluation, neural retrievalDid you distinguish recommendation from ad-hoc retrieval framing?
KDDLarge-scale mining, the sampled-metrics critiqueIs the scalability or evaluation-methodology neighbor acknowledged?
WSDM / TheWebConfWeb-scale recommendation, graph and CF methodsIs the nearest web-recommendation method compared?
UAIFoundational ranking (e.g., BPR) and probabilistic modelingAre canonical recommendation methods cited to their true venue, not RecSys?
Reproducibility line"Are we really making much progress?" and follow-upsDoes 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>
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