Wsdm artifact evaluation

WSDM Artifact Evaluation

Package artifacts for a venue where they are persuasion, not process. The pack found no formal artifact-evaluation track or badge system for current WSDM editions (待核实 each cycle) - the CFP-level expectation is the community norm of "practical yet principled": reviewers reward submissions whose claims a skeptic could re-derive. That means the artifact's job is to be inspectable during review and usable after publication, with no committee to certify it.

The reviewer-facing artifact

Because appendices count against WSDM's page budget and there is no rebuttal in which to hand over materials later, the anonymous repository referenced in the PDF is the only expansion space you get. Build it to be skimmed in ten minutes:

anon-artifact/
├── README.md            # 1 screen: claim -> script -> expected output table
├── LICENSE              # anonymized placeholder license during review
├── env/                 # lockfile or container spec, exact versions
├── data/
│   ├── public/          # download scripts for public benchmarks
│   └── PROPRIETARY.md   # honest statement of what cannot be shared and why
├── src/                 # training/ranking/mining code, no company paths
├── configs/             # one config per reported table row
└── results/
    └── seeds_1-5/       # raw metric dumps behind every table in the paper

Anonymity requirements are stricter than habit: fresh remote with no commit history, no author handles in configs or notebook metadata, no internal package registries, no dataset paths like /nfs/companyname/.... An Associate Chair can see who you are; your reviewers must not.

The proprietary-log dilemma

WSDM's core subject matter - query logs, click data, user graphs, ad interactions - is often legally unshareable. The venue's reviewers know this; what they punish is unverifiable work, not industrial work. Choose a rung and state it explicitly in the paper:

Rung	What is released	What the paper must then carry
Full release	Data + code + configs	Just the pointers
Sampled/anonymized release	A privacy-scrubbed sample + full code	The scrubbing procedure and how sample results track full-data results
Public-benchmark mirror	Code + experiments re-run on public datasets	Both result sets, with the deltas discussed, not hidden
Code-only	Pipeline code, no data	Enough dataset statistics that a platform-holder could replicate
Nothing sharable	-	A candid limitation; expect a proportional credibility discount

The public-benchmark mirror rung is the WSDM workhorse: pair the proprietary result with the same method on public data so at least one full row of the evidence is end-to-end reproducible by anyone.

Public substitutes worth knowing

• Standard IR/rec benchmarks with public tooling (MS MARCO, TREC collections, MovieLens/Amazon review dumps, KuaiRec-style log releases) - verify current licensing before citing a release plan.
• WSDM Cup datasets: the conference's own annual challenge (the 2027 site already carries a WSDM Cup call) periodically produces real industrial datasets; using one signals community alignment. Check the specific cup's redistribution terms.
• Graph/social benchmarks (SNAP collections and successors) for the network side of the venue.

Anything here can go stale or change license - re-verify at packaging time.

Model and prompt artifacts

Foundation-model-era WSDM papers ("Search with Foundation Models" is in-scope per the 2026 CFP) add artifact surfaces older guidance misses:

• Pin model identity precisely: provider, model string, version/date, and decoding parameters; an unpinned API model makes results unreproducible by construction - say what you did about that (snapshotting outputs, local checkpoints, fixed eval windows).
• Ship prompts verbatim in the repository even when the page budget forces the paper to summarize them.
• Cache and release model outputs used in evaluation when terms permit; that lets others audit the judgment layer without paying for inference.

Credibility signals at a badge-free venue

With no committee stamping artifacts, reviewers use fast proxies to decide whether the repository is real or decorative. Engineer the proxies:

Signal	Reads as	Cost to provide
Config file per reported table row	The numbers came from these runs	Minutes
Raw seed-level metric dumps	Variance claims are checkable	Minutes
A make reproduce-table2 entry point	Someone actually reran this	An hour
Download script for each public dataset	The pipeline is end-to-end	An hour
Honest PROPRIETARY.md	The authors know what they cannot prove	Minutes
Commit dated one hour before deadline as the only commit	Decorative repo	- avoid

The last row is about the squeeze: a repository assembled on deadline night tends to mismatch the paper's numbers, and one mismatch found by a reviewer discounts the entire artifact. Freeze the repo when experiments freeze (wsdm-workflow sets this at one week out), not when the PDF does.

Post-acceptance conversion

At camera-ready time the anonymous mirror becomes the citable artifact: move to the real organization/account, add the actual license, tag the release that matches the camera-ready numbers, and archive a snapshot (e.g., a DOI-issuing archive) so the URL in the ACM DL version outlives your CI. Update the README's claim-to-script table against final camera-ready table numbers - drift between repo and proceedings is the most common post-publication complaint.

One more conversion detail: if the paper used the public-benchmark-mirror rung, keep both pipelines in the public repo permanently - the mirror is what future papers will actually build on, and its issues page becomes your citation engine.

Output format

[Artifact tier] full / sampled / public-mirror / code-only / none + stated in paper? 
[Ten-minute test] README claim->script->output table present: yes / no
[Anonymity sweep] history / handles / paths / registries: clean or leaks listed
[FM pins] model string+date+decoding params recorded: yes / no / n-a
[Post-acceptance plan] public home, license, tagged release, archival snapshot