RRepoGEO

REPOGEO REPORT · LITE

datawhalechina/fun-rec

Default branch master · commit 13509d60 · scanned 6/18/2026, 5:57:46 AM

GitHub: 7,167 stars · 1,012 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
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 datawhalechina/fun-rec, 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
  • highhomepage#1
    Add the online tutorial URL as the repository homepage

    Why:

    COPY-PASTE FIX
    https://datawhalechina.github.io/fun-rec/
  • highlicense#2
    Add a LICENSE file to the repository root

    Why:

    COPY-PASTE FIX
    Add a `LICENSE` file to the repository root with the MIT License text.
  • mediumreadme#3
    Refine the README opening to explicitly state its nature as a comprehensive tutorial/book

    Why:

    CURRENT
    <div align=center>
    COPY-PASTE FIX
    本项目是一份全面系统的开源教程与实践指南（一本“书”），深入讲解推荐系统从传统级联架构到现代生成式范式的技术演进。它旨在帮助学习者和实践者掌握核心算法原理与工程实践。

<div align=center>

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 datawhalechina/fun-rec
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Recommender Systems Handbook
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Recommender Systems Handbook · recommended 1×
  2. Deep Learning for Recommender Systems · recommended 1×
  3. Coursera's 'Deep Learning Specialization' by Andrew Ng · recommended 1×
  4. Google's 'Recommendations AI' · recommended 1×
  5. Attention Is All You Need · recommended 1×
  • CATEGORY QUERY
    How to learn modern recommendation system algorithms, from traditional methods to generative AI paradigms?
    you: not recommended
    AI recommended (in order):
    1. Recommender Systems Handbook
    2. Deep Learning for Recommender Systems
    3. Coursera's 'Deep Learning Specialization' by Andrew Ng
    4. Google's 'Recommendations AI'
    5. Attention Is All You Need
    6. BERT4Rec

    AI recommended 6 alternatives but never named datawhalechina/fun-rec. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a practical guide for implementing deep learning and generative models in recommender systems.
    you: not recommended
    AI recommended (in order):
    1. TensorFlow (tensorflow/tensorflow)
    2. PyTorch (pytorch/pytorch)
    3. JAX (google/jax)
    4. Surprise (NicolasHug/Surprise)
    5. LightFM (lyst/lightfm)
    6. TensorFlow Recommenders (tensorflow/recommenders)
    7. Hugging Face Transformers (huggingface/transformers)
    8. Pandas (pandas-dev/pandas)
    9. Scikit-learn (scikit-learn/scikit-learn)
    10. TensorFlow Serving (tensorflow/serving)
    11. TorchServe (pytorch/serve)
    12. Kubeflow (kubeflow/kubeflow)

    AI recommended 12 alternatives but never named datawhalechina/fun-rec. 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 datawhalechina/fun-rec?
    pass
    AI named datawhalechina/fun-rec explicitly

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

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

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

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datawhalechina/fun-rec — 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