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>
