RRepoGEO

REPOGEO REPORT · LITE

grahamjenson/list_of_recommender_systems

Default branch master · commit 966106a6 · scanned 6/22/2026, 11:13:29 AM

GitHub: 4,838 stars · 704 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)

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

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 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 grahamjenson/list_of_recommender_systems, 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
    Emphasize the README's role as a curated directory of recommender systems

    Why:

    CURRENT
    Recommender systems (or recommendation engines) are useful and interesting pieces of software. I wanted to compare recommender systems to each other but could not find a decent list, so here is the one I created.
    COPY-PASTE FIX
    This repository serves as a **curated directory and comparison guide** for various recommender systems and resources. I created this list to help compare different recommendation engines, as a comprehensive resource was hard to find.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    recommender-systems, recommendation-engines, machine-learning, awesome-list, curated-list, resources, saas, algorithms, directory, comparison
  • highlicense#3
    Add a LICENSE file to clarify usage terms

    Why:

    CURRENT
    (no LICENSE file detected)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., CC-BY-4.0 for content or MIT for general use) in the repository root.

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
0 / 2
0% of queries surface grahamjenson/list_of_recommender_systems
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Amazon Personalize
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Amazon Personalize · recommended 2×
  2. Azure Personalizer · recommended 2×
  3. Gartner Peer Insights · recommended 1×
  4. Magic Quadrant · recommended 1×
  5. Forrester Wave Reports · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive list of available recommendation engine solutions?
    you: not recommended
    AI recommended (in order):
    1. Gartner Peer Insights
    2. Magic Quadrant
    3. Forrester Wave Reports
    4. Capterra
    5. G2
    6. Software Advice
    7. AWS Machine Learning Blog
    8. Amazon Personalize
    9. Google Cloud
    10. AI Platform
    11. Vertex AI
    12. Microsoft Azure
    13. Cognitive Services
    14. Azure Personalizer
    15. Shopify App Store
    16. Magento Marketplace
    17. Towards Data Science
    18. Medium

    AI recommended 18 alternatives but never named grahamjenson/list_of_recommender_systems. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are some good managed recommender system services for a new project?
    you: not recommended
    AI recommended (in order):
    1. Amazon Personalize
    2. Google Cloud Recommendations AI
    3. Azure Personalizer
    4. Algolia Recommend
    5. Recombee
    6. Qubit Aura
    7. Coveo

    AI recommended 7 alternatives but never named grahamjenson/list_of_recommender_systems. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    Suggestion:

  • 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 grahamjenson/list_of_recommender_systems?
    pass
    AI did not name grahamjenson/list_of_recommender_systems — likely talking about a different project

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

  • If a team adopts grahamjenson/list_of_recommender_systems in production, what risks or prerequisites should they evaluate first?
    pass
    AI named grahamjenson/list_of_recommender_systems 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 grahamjenson/list_of_recommender_systems solve, and who is the primary audience?
    pass
    AI did not name grahamjenson/list_of_recommender_systems — likely talking about a different project

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

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