RRepoGEO

REPOGEO REPORT · LITE

PreferredAI/cornac

Default branch master · commit a546e0e4 · scanned 6/27/2026, 2:31:50 PM

GitHub: 1,046 stars · 170 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
68 /100
Needs work
Category recall
1 / 2
Avg rank #3.0 when recommended
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface PreferredAI/cornac, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README opening to emphasize 'building' and 'Python library'

    Why:

    CURRENT
    Cornac is a comparative framework for multimodal recommender systems. It focuses on making it **convenient** to work with models leveraging **auxiliary data** (e.g., item descriptive text and image, social network, etc).
    COPY-PASTE FIX
    Cornac is a Python library and comparative framework for building and evaluating multimodal recommender systems. It focuses on making it **convenient** to work with models leveraging **auxiliary data** (e.g., item descriptive text and image, social network, etc).
  • mediumtopics#2
    Expand topics with more specific keywords

    Why:

    CURRENT
    collaborative-filtering, matrix-factorization, multimodal-learning, multimodality, recommendation-algorithms, recommendation-engine, recommendation-system, recommender-system
    COPY-PASTE FIX
    collaborative-filtering, matrix-factorization, multimodal-learning, multimodality, recommendation-algorithms, recommendation-engine, recommendation-system, recommender-system, python-library, deep-learning-recommenders, evaluation-framework, recsys-framework
  • lowreadme#3
    Add a 'Comparison' link to Quick Links in README

    Why:

    CURRENT
    The 'Quick Links' section in the README does not contain a link to a comparison section.
    COPY-PASTE FIX
    Add `[Comparison](#comparison) |` to the 'Quick Links' section in the README, for example, after `[Datasets](./cornac/datasets/README.md#datasets) |`.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
1 / 2
50% of queries surface PreferredAI/cornac
Avg rank
#3.0
Lower is better. #1 = top recommendation.
Share of voice
9%
Of all named tools, what % are you?
Top rival
Surprise
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Surprise · recommended 2×
  2. Spotlight · recommended 2×
  3. LightFM · recommended 1×
  4. TensorFlow Recommenders (TFRS) · recommended 1×
  5. PyTorch-Geometric (PyG) · recommended 1×
  • CATEGORY QUERY
    Seeking a Python library for building multimodal recommender systems with auxiliary data.
    you: not recommended
    AI recommended (in order):
    1. LightFM
    2. Surprise
    3. Spotlight
    4. TensorFlow Recommenders (TFRS)
    5. PyTorch-Geometric (PyG)

    AI recommended 5 alternatives but never named PreferredAI/cornac. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework helps evaluate and compare different recommendation algorithms effectively?
    you: #3
    AI recommended (in order):
    1. RecBole
    2. Surprise
    3. Cornac ← you
    4. LensKit
    5. Spotlight
    6. LibRec
    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of PreferredAI/cornac?
    pass
    AI named PreferredAI/cornac explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts PreferredAI/cornac in production, what risks or prerequisites should they evaluate first?
    pass
    AI named PreferredAI/cornac explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo PreferredAI/cornac solve, and who is the primary audience?
    pass
    AI named PreferredAI/cornac explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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PreferredAI/cornac — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite