Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-13 07:28 UTC

PreferredAI / cornac

A Comparative Framework for Multimodal Recommender Systems

Python · CythonApache-2.0★ 1,055 stars⑂ 172 forkssince Jul 2018View on GitHub ↗

PreferredAI/cornac holds a health index of 75 out of 100, placing it in the Good band. It scores highest on Vitality (79/100) and lowest on Security (21/100). It was last updated today. A single contributor accounts for most of its recent work.

75
overall / 100
Good

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

75
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 65 is calibrated to 75 on the published index scale (record calibration 2026-08-02).

Ownership

Preferred.AIOrganization
64 followers52 public repossince Nov 2017

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIcornac3.0.031,8596916 days agorecommender-systemcollaborative-filteringmultimodalpreference-learningrecommendation

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

79Good · 21% of overall
How it's scored
36/36Push recency — last push 0 days ago
11.8/36Commit cadence — 17/52 weeks with commits
14.8/18Commit volume — 43 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year43
human_commit_share1
days_since_last_push0
active_weeks_last_year17
How it's scored
27/27Ships releases — 64 releases published
36/36Release recency — latest release 16 days ago
19.8/27Release cadence — a release every ~67.9 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count64
latest_release_tagv3.0.0
releases_from_tagsno
days_since_latest_release16
mean_days_between_releases67.9

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

70Good · 17% of overall
How it's scored
49/60Stars — 1,055 stars
18.6/25Forks — 172 forks
7.4/15Watchers — 22 watchers
Inputs used
forks172
stars1,055
watchers22
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
0/18CONTRIBUTING guide
0/13.5Code of conduct
7.2/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges14
has_contributingno
has_issue_templateyes
has_code_of_conductno
readme_badge_servicesdev.azure.com, github.com, shields.io
has_pull_request_templateyes

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

71Good · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
10.7/22.5Commit distribution — top contributor authored 52% of commits
13.5/13.5Contributor breadth — 28 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled28
top_contributor_share0.524
How it's scored
36.2/42Issue resolution — 86% of issues closed
27.2/30PR acceptance — 495/545 decided PRs merged
13/13Newcomer PR acceptance — 1/1 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs495
open_issues24
closed_issues149
prs_merged_7d1
prs_decided_7d1
prs_merged_30d3
prs_decided_30d3
issue_closed_ratio0.861
closed_unmerged_prs50
first_time_authors_30d1
first_time_prs_merged_30d1
first_time_prs_decided_30d1
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
13/25Owner reach — 64 followers of PreferredAI
24.6/25Track record — 52 public repos, account ~8 yr old
Inputs used
followers64
owner_typeOrganization
is_verifiedno
owner_loginPreferredAI
public_repos52
account_age_days3,209

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 16 days ago
20/20Version history — 69 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagescornac
ecosystemspypi
any_deprecatedno
min_days_since_publish16

Engineering Quality

Are baseline engineering and documentation practices in place?

76Good · 19% of overall
How it's scored
24/24CI workflows — 4 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://cornac.preferred.ai
10/10Repository description
10/10Topics — 8 topics
10/10Wiki
Inputs used
topicsrecommender-system, recommendation-algorithms, recommendation-engine, matrix-factorization, collaborative-filtering, multimodal-learning, recommendation-system, multimodality
has_wikiyes
homepagehttps://cornac.preferred.ai
docs_sitehttps://cornac.preferred.ai
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

21At Risk · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles — published library — lockfiles are an application concern, not expected
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestsdocs/requirements.txt, pyproject.toml, requirements.txt, setup.cfg, setup.py
has_codeql_workflowno
has_security_policyno
has_dependabot_configno
Excluded from scoring (no data or not applicable): Dependency lockfiles. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages15
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:cornac@3.0.0 runtime dependency closure — what installing the published package pulls in — 15 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

60Moderate · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
39.5/40Legible commit history — 74 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.74
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
18/18One-command bootstrap — docs/Makefile
22/22Automated tests
0/11Lint / format config
0/11Static type checking
10/10Reproducible environment — Dockerfile
6/10Demonstrated agent practice — 3 of the last 100 commits agent-authored or agent-credited
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_filesdocs/Makefile
has_devcontainerno
has_linter_configno
typecheck_configs—
agent_commit_share0.03
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Python without a type-check config
54.3/55Manageable file sizes — 44/3,578 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes1,058,368
source_files_sampled3,578
oversized_source_files44
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — examples, notebooks
Inputs used
example_dirsexamples, notebooks
has_mcp_signalno
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

1,055GitHub stars
28contributors
43commits, last 12 months
0days since last push
64releases
1bus factor
24open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • Could not fetch pypi package 'cornac.models.c2pf.c2pf' from its registry
  • OpenSSF Scorecard did not return a usable result (killed by SIGKILL — most likely the container's memory limit); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 172 ⇿
0Stars
172Forks
64Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

0408012016020016962018-102022-092026-09
Major 3Minor 26Patch 34

Each point covers 8 days.

Direct dependencies 4
RegistryPackageVersion constraintManifest
PyPInumpy>2.0.0pyproject.toml
PyPIscipy—pyproject.toml
PyPItqdm—pyproject.toml
PyPIpowerlaw—pyproject.toml
All dependencies 42

Full resolved dependency set from the GitHub dependency graph: 5 direct and 37 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPInumpy—direct
PyPIpowerlaw—direct
PyPIscipy—direct
PyPIscipy1.2.1direct
PyPItqdm—direct
PyPIcython—indirect
PyPIdcor—indirect
PyPIdgl—indirect
PyPIdgl1.1.0indirect
PyPIdgl2.4.0indirect
PyPIdgl2.4.0+cu118indirect
PyPIfaiss-cpu—indirect
PyPIfilelock3.8.2indirect
PyPIflask—indirect
PyPIhuggingface-hub—indirect
PyPIk-means-constrained—indirect
PyPImyst-parser—indirect
PyPInltk—indirect
PyPIpandas—indirect
PyPIpandas2.2.3indirect
PyPIpydata-sphinx-theme—indirect
PyPIpytest—indirect
PyPIpytest-cov—indirect
PyPIpytest-xdist—indirect
PyPIrequests—indirect
PyPIsansa—indirect
PyPIscikit-learn—indirect
PyPIsentence-transformers—indirect
PyPIsentence-transformers2.2.2indirect
PyPIsetuptools—indirect
PyPIsimilaripy0.1.3indirect
PyPIsphinx-copybutton—indirect
PyPIsphinx-design—indirect
PyPItensorflow—indirect
PyPItensorflow1.15.4indirect
PyPItensorflow2.12.0indirect
PyPItensorflow2.21.0indirect
PyPItorch—indirect
PyPItorch2.0.0indirect
PyPItorch2.3.*indirect
PyPItorchvision—indirect
PyPItransformers—indirect
Dependency advisories 0

Installing pypi:cornac@3.0.0 pulls in 15 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.