Use this to model decisions rather than guess at them. KDD review runs on OpenReview,
per track and per cycle (the venue groups are literally named by track x cycle, e.g.
Research_Track_Cycle_2). Reconfirm the current cycle's mechanics before relying on
any stage detail — KDD tunes its process between cycles, not just between years.
Unlike single-shot venues, KDD's decision space includes Resubmit, and the CFP frames resubmissions that properly address noted concerns as having better odds than fresh submissions. Strategic consequences:
| Outcome | What it means | Author's next move |
|---|---|---|
| Accept | Proceedings slot (uniform 12-page budget) | Camera-ready via e-rights + TAPS (kdd-camera-ready) |
| Resubmit | Fixable weaknesses named; invitation to the next cycle | Address concerns, declare prior forum id, prepend one-page change summary (kdd-supplementary) |
| Reject | Fit or soundness failure | Re-route (kdd-topic-selection) or rebuild before any KDD return |
Treat reviews of a Resubmit paper as a contract: the next cycle's AC sees the old forum, so selectively ignoring named concerns is visible and costly.
The pool mixes academic data-mining researchers with industry practitioners — a KDD-specific blend with predictable reading patterns:
Triage order for a 3-4 review packet:
1. Extract every sentence naming a missing experiment, baseline, or
leakage risk -> these are Resubmit-contract items.
2. Classify each reviewer: academic-lens / practitioner-lens / unclear
(their objections need different evidence types in rebuttal).
3. Find the AC-visible consensus: an objection raised independently
twice outweighs any single reviewer's pet issue.
4. Score your realistic ceiling: all-borderline packets are rebuttal-
winnable; a unanimous soundness objection is a next-cycle project.
The CFP's factor list is long; leverage over it is not uniform. Where author effort converts into recommendation movement:
| Decision factor | Raises it | Sinks it |
|---|---|---|
| Technical merit | Mechanism isolated by ablation; complexity stated and measured | Gains attributable to tuning asymmetry or leakage |
| Originality | Mechanism-level delta over the KDD lineage | "First to apply X to Y" with no structural argument |
| Potential impact | Evidence someone else can use it (artifact, generality across regimes) | Impact claimed via market-size rhetoric |
| Execution quality | Temporal-safe splits, strong boring baselines | One-seed results at the flagship scale claim |
| Presentation | Regime-first page one; findable evidence | Appendix doing the arguing |
| Related work | Nearest ancestors contrasted, venues correct | Misattributed classics; surveyed families without deltas |
| Reproducibility | Tier-honest availability statements | Paper-artifact contradictions |
| Ethics | Data provenance and consent story stated | Scraped-data hand-waving on human data |
kdd-submission).[Stage] submitted / reviews-in / rebuttal / decision / resubmit-window
[Packet read] R-lenses: <academic/practitioner mix>, consensus objection: <...>
[Realistic ceiling] accept / borderline-rebuttal-decides / resubmit-target
[Resubmit contract] <named concerns that must be addressed if returning>
[Integrity checks] genAI disclosure filed / confidentiality clean
[Next move] <one action with owner>