RRepoGEO

REPOGEO REPORT · LITE

amitkaps/recommendation

Default branch master · commit 7f7ecbdd · scanned 6/11/2026, 7:17:53 AM

GitHub: 534 stars · 164 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 amitkaps/recommendation, 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 the README H1 and opening paragraph to clarify its workshop/primer nature

    Why:

    CURRENT
    # Recommendation Systems
This is a workshop on using Machine Learning and Deep Learning Techniques to build Recommendation Systesm
    COPY-PASTE FIX
    # Recommendation Systems Workshop: Build ML/DL Recommenders from Scratch
This repository provides a comprehensive workshop and primer for building recommendation systems using Machine Learning and Deep Learning techniques, focusing on practical examples and step-by-step implementations rather than a production-ready library.
  • mediumabout#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/amitkaps/recommendation#notebooks
  • lowtopics#3
    Augment existing topics to emphasize the educational and example-driven nature

    Why:

    CURRENT
    colloborative-filtering, deep-learning, hybrid-recommender, primer, recsys, workshop
    COPY-PASTE FIX
    colloborative-filtering, deep-learning, hybrid-recommender, primer, recsys, workshop, tutorial, examples, machine-learning-tutorial, educational

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 amitkaps/recommendation
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Surprise
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Surprise · recommended 2×
  2. LightFM · recommended 2×
  3. TensorFlow · recommended 1×
  4. PyTorch · recommended 1×
  5. Scikit-learn · recommended 1×
  • CATEGORY QUERY
    How to build a personalized recommendation engine using machine learning and deep learning?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow
    2. PyTorch
    3. Surprise
    4. LightFM
    5. Scikit-learn
    6. Apache Spark MLlib
    7. RecBole

    AI recommended 7 alternatives but never named amitkaps/recommendation. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a Python library for collaborative filtering or hybrid recommendation systems.
    you: not recommended
    AI recommended (in order):
    1. Surprise
    2. LightFM
    3. implicit
    4. scikit-learn
    5. TensorFlow Recommenders (TFRS)
    6. PyTorch-Recommender

    AI recommended 6 alternatives but never named amitkaps/recommendation. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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 amitkaps/recommendation?
    pass
    AI named amitkaps/recommendation explicitly

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

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

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

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amitkaps/recommendation — 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