RRepoGEO

REPOGEO REPORT · LITE

QunBB/DeepLearning

Default branch main · commit 48ced056 · scanned 6/10/2026, 10:33:07 AM

GitHub: 507 stars · 82 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
23 /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
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 QunBB/DeepLearning, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT License or Apache-2.0 License) in the root of the repository to clearly state the terms of use for the code.
  • highreadme#2
    Add a concise, high-level overview to the README's beginning

    Why:

    CURRENT
    The README currently starts with specific implementation details like "# 1. tensorflow使用".
    COPY-PASTE FIX
    This repository is a comprehensive resource for Deep Learning, with a strong focus on **Recommendation Systems** (including CTR prediction, matching, and ranking models like DeepFM, DCN, DIN, DIEN, MMoE, PLE), **Natural Language Processing**, and practical implementations using **TensorFlow** and **PyTorch**. It provides detailed code examples and explanations for various advanced deep learning concepts.

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 QunBB/DeepLearning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepFM
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepFM · recommended 1×
  2. DCN · recommended 1×
  3. xDeepFM · recommended 1×
  4. DIN · recommended 1×
  5. DIEN · recommended 1×
  • CATEGORY QUERY
    What are common deep learning models for improving CTR prediction in recommendation systems?
    you: not recommended
    AI recommended (in order):
    1. DeepFM
    2. DCN
    3. xDeepFM
    4. DIN
    5. DIEN
    6. Wide & Deep Learning
    7. AutoInt

    AI recommended 7 alternatives but never named QunBB/DeepLearning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I implement multi-task learning models like MMOE or PLE for recommendation tasks?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow (with Keras API) (tensorflow/tensorflow)
    2. PyTorch (pytorch/pytorch)
    3. DeepCTR-Torch (shenweichen/DeepCTR-Torch)
    4. LibRecommender (massquantity/LibRecommender)
    5. PaddlePaddle (with PaddleRec) (PaddlePaddle/Paddle)

    AI recommended 5 alternatives but never named QunBB/DeepLearning. 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 QunBB/DeepLearning?
    pass
    AI named QunBB/DeepLearning explicitly

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

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