Individual skill
wsdm-reproducibility
Use when hardening the reproducibility of a WSDM paper built on logs, graphs, or user-interaction data - provenance of behavioral datasets, temporal split discipline, click-bias assumptions, seed and variance reporting, privacy-preserving release, and honesty tiers for results no outsider can rerun.
WSDM-Skills/skills/wsdm-reproducibility/SKILL.md
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# WSDM Reproducibility
Make a web-search/data-mining paper re-derivable. WSDM has no reproducibility
checklist to fill (none surfaced for current editions; 待核实 each cycle) - which
raises the bar rather than lowering it, because reviewers apply the norm
without giving you a form to hide behind. The venue-specific twist: WSDM
evidence usually comes from *behavioral* data (queries, clicks, follows,
purchases), and behavioral data has failure modes that generic ML
reproducibility advice never mentions.
## Provenance: behavioral data decays
A log dataset is a measurement of a platform at a moment - the platform's
ranker, UI, and user base are all baked into it. Reproducibility therefore