RecSys Topic Selection
Use this before writing. RecSys is a single-domain venue: it is strongest when the central
claim is about recommendation — ranking objectives, user/item modeling, offline/online
evaluation, feedback loops, exposure and fairness, or deployed recommender behavior. Being merely
applicable to recommendation is not enough; the contribution has to speak to a recommender
audience.
Fit test
- Prefer RecSys when the main claim is a recommendation result: a ranking objective, a
user/session model, an evaluation protocol, an off-policy/counterfactual method, a
fairness/diversity/exposure result, or a deployed-system insight.
- Route to SIGIR when the core is ad-hoc retrieval, search ranking, or IR evaluation not tied
to recommendation.
- Route to KDD when the contribution is a general data-mining or large-scale algorithm and
recommendation is just one application.
- Route to WSDM / TheWebConf (WWW) when the emphasis is web-scale search-and-mining or web
systems broadly.
- Route to UAI or an ML venue when the contribution is a general learning/probabilistic
advance rather than recommender-specific evidence.
- Route to CHI / CSCW when user or community outcomes dominate over the recommendation
algorithm.
Fit signal table
| Signal in the project |
RecSys reading |
| A ranking/user-modeling idea evaluated with tuned baselines and a leakage-aware split |
Core fit — the house genre |
| Off-policy or counterfactual evaluation of recommendations |
Core fit — a RecSys distinctive |
| A deployed system with production constraints and A/B evidence |
Core fit — route to the Industry track |
| A reproduction/refutation of prior recommender results |
Core fit — route to the Reproducibility track |
| A general retrieval or mining method, recommendation as one demo |
Better at SIGIR / KDD / WSDM |
| A general ML method with a recommender benchmark tacked on |
Better at an ML venue |
Which RecSys track
- Main long paper: a rounded recommendation contribution with offline (and ideally online)
evidence.
- Main short / Past-Present-Future: one focused finding, or a reflective/forward-looking position.
- Reproducibility: repeating, refuting, or re-scoping prior results; a dataset or framework.
- Industry: a deployed system with production constraints and live evidence.
- Resource / Dataset: a community dataset or software resource with build methodology.
Vignette: where an exposure-correction method goes
A project delivers an off-policy ranker with an exposure-corrected estimator and a simulator
bridge. RecSys reading: strong fit — a recommendation-specific evaluation advance is exactly what
the venue rewards, Main long paper. Strip the recommendation framing and keep only a generic
off-policy estimator, and it drifts toward an ML venue; turn it into a reproduction of three
published rankers, and the Reproducibility track becomes the right home.
Sharpening moves before committing
- Name the recommendation primitive: the ranking objective, the user/item model, the evaluation
protocol, or the deployment claim. If none exists, the RecSys framing does not.
- Confirm the evidence can meet the venue's evaluation bar (tuned baselines, leakage-aware split,
reported variance) — decoration-only benchmarks are a quiet fit failure here.
- Topic emphasis and the track lineup drift between cycles (2026 dropped LBR, added R&P Notes);
scan the current CFP before final routing.
Output format
[Fit] strong RecSys / possible RecSys / better elsewhere
[Best venue] RecSys / SIGIR / KDD / WSDM / TheWebConf / UAI / CHI / ML venue / other
[RecSys track] main-long / main-short / past-present-future / reproducibility / industry / resource
[Contribution sentence] <one sentence>
[Top rejection risk] <novelty / evaluation validity / scope / fit>
[Next action] <experiment, framing, or venue switch>
1---2name: recsys-topic-selection3description: Use when deciding whether a project is a strong ACM RecSys fit, routing among RecSys, SIGIR, KDD, WSDM, TheWebConf, UAI, CHI, and general ML venues, identifying whether the core contribution is genuinely about recommendation, and choosing the right RecSys track before writing begins.4---56# RecSys Topic Selection78Use this before writing. RecSys is a **single-domain** venue: it is strongest when the central9claim is about **recommendation** — ranking objectives, user/item modeling, offline/online10evaluation, feedback loops, exposure and fairness, or deployed recommender behavior. Being merely11*applicable* to recommendation is not enough; the contribution has to speak to a recommender12audience.1314## Fit test1516- Prefer RecSys when the main claim is a recommendation result: a ranking objective, a17 user/session model, an evaluation protocol, an off-policy/counterfactual method, a18 fairness/diversity/exposure result, or a deployed-system insight.19- Route to **SIGIR** when the core is ad-hoc retrieval, search ranking, or IR evaluation not tied20 to recommendation.21- Route to **KDD** when the contribution is a general data-mining or large-scale algorithm and22 recommendation is just one application.23- Route to **WSDM / TheWebConf (WWW)** when the emphasis is web-scale search-and-mining or web24 systems broadly.25- Route to **UAI** or an ML venue when the contribution is a general learning/probabilistic26 advance rather than recommender-specific evidence.27- Route to **CHI / CSCW** when user or community outcomes dominate over the recommendation28 algorithm.2930## Fit signal table3132| Signal in the project | RecSys reading |33|---|---|34| A ranking/user-modeling idea evaluated with tuned baselines and a leakage-aware split | Core fit — the house genre |35| Off-policy or counterfactual evaluation of recommendations | Core fit — a RecSys distinctive |36| A deployed system with production constraints and A/B evidence | Core fit — route to the Industry track |37| A reproduction/refutation of prior recommender results | Core fit — route to the Reproducibility track |38| A general retrieval or mining method, recommendation as one demo | Better at SIGIR / KDD / WSDM |39| A general ML method with a recommender benchmark tacked on | Better at an ML venue |4041## Which RecSys track4243- Main long paper: a rounded recommendation contribution with offline (and ideally online)44 evidence.45- Main short / Past-Present-Future: one focused finding, or a reflective/forward-looking position.46- Reproducibility: repeating, refuting, or re-scoping prior results; a dataset or framework.47- Industry: a deployed system with production constraints and live evidence.48- Resource / Dataset: a community dataset or software resource with build methodology.4950## Vignette: where an exposure-correction method goes5152A project delivers an off-policy ranker with an exposure-corrected estimator and a simulator53bridge. RecSys reading: strong fit — a recommendation-specific evaluation advance is exactly what54the venue rewards, Main long paper. Strip the recommendation framing and keep only a generic55off-policy estimator, and it drifts toward an ML venue; turn it into a reproduction of three56published rankers, and the Reproducibility track becomes the right home.5758## Sharpening moves before committing5960- Name the recommendation primitive: the ranking objective, the user/item model, the evaluation61 protocol, or the deployment claim. If none exists, the RecSys framing does not.62- Confirm the evidence can meet the venue's evaluation bar (tuned baselines, leakage-aware split,63 reported variance) — decoration-only benchmarks are a quiet fit failure here.64- Topic emphasis and the track lineup drift between cycles (2026 dropped LBR, added R&P Notes);65 scan the current CFP before final routing.6667## Output format6869```text70[Fit] strong RecSys / possible RecSys / better elsewhere71[Best venue] RecSys / SIGIR / KDD / WSDM / TheWebConf / UAI / CHI / ML venue / other72[RecSys track] main-long / main-short / past-present-future / reproducibility / industry / resource73[Contribution sentence] <one sentence>74[Top rejection risk] <novelty / evaluation validity / scope / fit>75[Next action] <experiment, framing, or venue switch>76```
brycewang-stanford