Position a submission against the literature the WSDM PC actually knows. At a small single-track venue, related-work errors are unusually visible: the person who wrote the paper you mis-cited may be your SPC. The section's job in a 9-page-inclusive budget is positioning - establishing which conversation the paper joins and what precisely it adds - not coverage.
WSDM has multi-edition research lineages, and reviewers instinctively slot new
work into them. Naming your lineage does the slotting for them (all rows
verified against ACM DL/dblp; see ../../resources/exemplars/library.md):
| Lineage | Anchor papers at WSDM | If your paper is here, position against |
|---|---|---|
| Click models / position bias | Craswell et al. 2008 (cascade model) | Subsequent click-model and propensity work |
| Unbiased learning-to-rank | Joachims et al. 2017 | The ULTR line it started, incl. recent WSDM/SIGIR follow-ups |
| Sequential recommendation | Tang & Wang 2018 (Caser) | The CNN/attention sequential-rec succession |
| RL / bandits for recommendation | Chen et al. 2019 (Top-K off-policy, YouTube) | Off-policy and bandit rec work since |
| Community detection / graph mining | Yang & Leskovec 2013 (BigCLAM) | Scalable overlapping-community successors |
If no WSDM lineage fits, that is routing information, not a citation problem -
run wsdm-topic-selection before writing further.
For each of the three to five closest works, write one sentence with this anatomy: their mechanism, their setting, the delta, why the delta matters here. Citation-listing ("[3,7,12] studied recommendation") communicates nothing and spends budget.
Template:
<Closest work> <does X by mechanism M> <in setting S>; we differ by <Δ>,
which matters because <consequence in our data regime>.
Instance (fictional):
CDR-Net transfers cross-domain preferences via shared user embeddings,
assuming overlapping user sets; we require no user overlap, which is the
common case for the partner-site logs we target.
Reviewers at this venue read contrast sentences as competence signals: they show you know the mechanism, not just the title.
WSDM's neighborhood is dense - SIGIR, KDD, TheWebConf (WWW), CIKM, RecSys, and ICWSM publish adjacent work continuously. Rules:
Famous papers cluster in this neighborhood, and attributing them to the wrong venue damages credibility with reviewers who know exactly where they appeared. Verified placements to guard against common mix-ups:
When in doubt, resolve the venue on dblp before the citation enters the draft; never trust memory or a secondhand BibTeX file for venue fields.
A dwell-time debiasing paper joining the unbiased-LTR lineage might position itself in four sentences - lineage entry, two contrasts, one boundary:
Learning from logged interactions without inheriting their biases is a
long-standing WSDM concern, from cascade-style click models [Craswell et
al., 2008] to counterfactual learning-to-rank [Joachims et al., 2017].
Propensity-based ULTR corrects exposure bias but treats post-click signals
as unbiased; we show dwell time carries its own salience confound and
extend the counterfactual framework to post-click behavior. Session-aware
rerankers <fictional cites> model dwell directly but require editorial
labels for calibration; our estimator calibrates from abandonment
behavior alone. Unlike both lines, we assume no access to the production
propensity model, matching the partner-platform setting of Section 5.
Note what the paragraph never does: survey the field, praise prior work emptily, or cite anything it does not contrast. Each sentence moves the paper's coordinates.
Target half a page to three-quarters. Structure that survives compression:
Where the intro already contrasts the closest work (it should - see
wsdm-writing-style), the related-work section elaborates rather than
repeats: same contrast, mechanism-level detail.
[Lineage] WSDM lineage(s) joined: <named or "none - route check">
[Contrast sentences] closest 3-5 works each have mechanism-level contrast: yes / gaps
[Neighbor sweep] WSDM/SIGIR/KDD/WWW/CIKM last-2-editions checked: date + hits
[Misattribution scan] venue fields dblp-verified: yes / fixes made
[Budget] section length vs target: pass / compress list