RRepoGEO

REPOGEO REPORT · LITE

chocoluffy/deep-recommender-system

Default branch master · commit 320b7d6f · scanned 5/27/2026, 8:58:44 AM

GitHub: 805 stars · 183 forks

AI VISIBILITY SCORE
28 /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
2 / 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 chocoluffy/deep-recommender-system, 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 clearly state purpose and audience

    Why:

    CURRENT
    # 目录
    COPY-PASTE FIX
    This repository provides a curated collection of deep learning implementations and engineering insights for building advanced recommendation systems. It covers key architectures, practical tricks, and paper summaries, making it a valuable resource for researchers and practitioners in personalized recommendation.

# 目录
  • mediumlicense#2
    Add a LICENSE file to clarify usage terms

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the root directory, choosing a standard open-source license that reflects your intended usage terms (e.g., MIT, Apache-2.0, GPL-3.0).
  • lowhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Add a relevant URL (e.g., a project page, a personal blog post about the project, or the GitHub repo URL itself) to the 'Homepage' field in the repository settings.

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 chocoluffy/deep-recommender-system
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
YouTube's DNN
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. YouTube's DNN · recommended 1×
  2. Google's Two-Stage Recommender · recommended 1×
  3. ScaNN · recommended 1×
  4. FAISS · recommended 1×
  5. NCF · recommended 1×
  • CATEGORY QUERY
    What are effective deep learning architectures for building personalized recommendation systems?
    you: not recommended
    AI recommended (in order):
    1. YouTube's DNN
    2. Google's Two-Stage Recommender
    3. ScaNN
    4. FAISS
    5. NCF
    6. GMF
    7. MLP
    8. NeuMF
    9. Wide & Deep Learning (Google)
    10. SASRec
    11. BERT4Rec
    12. PinSage (Pinterest)
    13. LightGCN
    14. DAE (Denoising Autoencoders for Recommendation)

    AI recommended 14 alternatives but never named chocoluffy/deep-recommender-system. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to apply advanced deep learning techniques for improving e-commerce recommendation accuracy?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow
    2. Keras
    3. PyTorch
    4. LightFM
    5. DeepCTR
    6. RecBole
    7. Surprise
    8. PyTorch Geometric (PyG)
    9. Deep Graph Library (DGL)

    AI recommended 9 alternatives but never named chocoluffy/deep-recommender-system. 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 chocoluffy/deep-recommender-system?
    pass
    AI named chocoluffy/deep-recommender-system explicitly

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

  • If a team adopts chocoluffy/deep-recommender-system in production, what risks or prerequisites should they evaluate first?
    pass
    AI named chocoluffy/deep-recommender-system 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 chocoluffy/deep-recommender-system solve, and who is the primary audience?
    pass
    AI did not name chocoluffy/deep-recommender-system — 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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chocoluffy/deep-recommender-system — 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