RRepoGEO

REPOGEO REPORT · LITE

gorse-io/gorse

Default branch master · commit 9f7632a3 · scanned 6/20/2026, 4:41:52 AM

GitHub: 9,724 stars · 901 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
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 gorse-io/gorse, 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 intro to clarify Gorse is a complete engine

    Why:

    CURRENT
    Gorse is an AI powered open-source recommender system written in Go. Gorse aims to be a universal open-source recommender system that can be quickly integrated into a wide variety of online services. By importing items, users, and interaction data into Gorse, the system will automatically train models to generate recommendations for each user. Project features are as follows.
    COPY-PASTE FIX
    Gorse is an AI powered open-source recommender system written in Go. Gorse aims to be a universal open-source recommender system that can be quickly integrated into a wide variety of online services. Unlike general ML libraries, vector databases, or web frameworks, Gorse provides a complete, self-contained engine for personalized recommendations. By importing items, users, and interaction data into Gorse, the system will automatically train models to generate recommendations for each user. Project features are as follows.
  • mediumtopics#2
    Add specific topics to highlight key differentiators

    Why:

    CURRENT
    collaborative-filtering, go, knn, machine-learning, recommender-system
    COPY-PASTE FIX
    collaborative-filtering, go, knn, machine-learning, recommender-system, multimodal, llm, api, production-ready, recommendation-engine
  • lowreadme#3
    Clarify the role of RESTful APIs in the features list

    Why:

    CURRENT
    RESTful APIs: Expose RESTful APIs for data CRUD and recommendation requests.
    COPY-PASTE FIX
    RESTful APIs: Expose RESTful APIs for data CRUD and recommendation requests, enabling seamless integration of the Gorse engine into any application without needing to build custom API layers.

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 gorse-io/gorse
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Milvus
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Milvus · recommended 1×
  2. Weaviate · recommended 1×
  3. Hugging Face Transformers · recommended 1×
  4. CLIP · recommended 1×
  5. BLIP / BLIP-2 · recommended 1×
  • CATEGORY QUERY
    How to implement an open-source AI recommendation engine supporting multimodal content?
    you: not recommended
    AI recommended (in order):
    1. Milvus
    2. Weaviate
    3. Hugging Face Transformers
    4. CLIP
    5. BLIP / BLIP-2
    6. ImageBind
    7. Faiss
    8. Scikit-learn
    9. Surprise
    10. Apache Spark MLlib
    11. TensorFlow Recommenders (TFRS)
    12. PyTorch-Ignite

    AI recommended 12 alternatives but never named gorse-io/gorse. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good open-source recommender systems written in Go with RESTful APIs?
    you: not recommended
    AI recommended (in order):
    1. Go-Recommender (zhenghaoz/Go-Recommender)
    2. Gin (gin-gonic/gin)
    3. Echo (labstack/echo)
    4. Go-RecSys (wangzheng/go-recsys)
    5. TensorFlow (tensorflow/tensorflow)
    6. TensorFlow Recommenders (tensorflow/recommenders)
    7. TensorFlow Serving (tensorflow/serving)
    8. Apache Mahout (apache/mahout)
    9. Surprise (NicolasHug/Surprise)
    10. Flask (pallets/flask)
    11. FastAPI (tiangolo/fastapi)

    AI recommended 11 alternatives but never named gorse-io/gorse. This is the gap to close.

    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 gorse-io/gorse?
    pass
    AI named gorse-io/gorse explicitly

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

  • If a team adopts gorse-io/gorse in production, what risks or prerequisites should they evaluate first?
    pass
    AI named gorse-io/gorse 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 gorse-io/gorse solve, and who is the primary audience?
    pass
    AI named gorse-io/gorse explicitly

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

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gorse-io/gorse — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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