RRepoGEO

REPOGEO REPORT · LITE

datawhalechina/torch-rechub

Default branch main · commit b3d7b798 · scanned 5/22/2026, 9:57:03 AM

GitHub: 1,148 stars · 145 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 datawhalechina/torch-rechub, 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
  • hightopics#1
    Refine repository topics for sharper focus

    Why:

    CURRENT
    ascend, ctr-prediction, deep-learning, generative-recommendation, hstu, llm, npu, onnx, pytorch, recommendation-algorithms, recommendation-engine, recommendation-system, recommender-system, recsys
    COPY-PASTE FIX
    ascend, ctr-prediction, deep-learning, generative-recommendation, npu, onnx, pytorch, recommendation-algorithms, recommendation-engine, recommendation-system, recommender-system, recsys
  • highreadme#2
    Strengthen README's opening paragraph to emphasize comprehensive framework nature

    Why:

    CURRENT
    Torch-RecHub —— Build production-grade recommender systems in 10 lines of code. 30+ mainstream models out-of-the-box, one-click ONNX deployment, letting you focus on business instead of engineering.
    COPY-PASTE FIX
    Torch-RecHub is a comprehensive PyTorch framework designed for building and deploying production-grade recommender systems. It serves as a central hub, offering 30+ mainstream models out-of-the-box and one-click ONNX deployment, enabling you to focus on business logic rather than engineering complexities.
  • mediumcomparison#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    ## 💡 Comparison with Alternatives

[Provide a brief comparison of Torch-RecHub with other popular PyTorch-based recommendation frameworks like DeepCTR-Torch, RecBole, or Merlin, highlighting key strengths such as model variety, ease of deployment, hardware support (Ascend/NPU), or specific features.]

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/torch-rechub
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch-Geometric (PyG)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch-Geometric (PyG) · recommended 1×
  2. PyTorchLightning/pytorch-lightning · recommended 1×
  3. shenweichen/DeepCTR-Torch · recommended 1×
  4. RUCAIBox/RecBole · recommended 1×
  5. NVIDIA/Merlin · recommended 1×
  • CATEGORY QUERY
    How to quickly implement deep learning recommendation models using PyTorch?
    you: not recommended
    AI recommended (in order):
    1. PyTorch-Geometric (PyG)

    AI recommended 1 alternative but never named datawhalechina/torch-rechub. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best PyTorch frameworks for building scalable recommender systems?
    you: not recommended
    AI recommended (in order):
    1. PyTorch-Lightning (PyTorchLightning/pytorch-lightning)
    2. DeepCTR-Torch (shenweichen/DeepCTR-Torch)
    3. RecBole (RUCAIBox/RecBole)
    4. Merlin (NVIDIA/Merlin)
    5. NVTabular (NVIDIA/NVTabular)
    6. HugeCTR (NVIDIA/HugeCTR)
    7. Triton Inference Server (triton-inference-server/server)
    8. TorchRec (pytorch/torchrec)
    9. PyTorch Geometric (pyg-team/pytorch_geometric)

    AI recommended 9 alternatives but never named datawhalechina/torch-rechub. 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 datawhalechina/torch-rechub?
    pass
    AI named datawhalechina/torch-rechub 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/torch-rechub in production, what risks or prerequisites should they evaluate first?
    pass
    AI named datawhalechina/torch-rechub 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/torch-rechub solve, and who is the primary audience?
    pass
    AI named datawhalechina/torch-rechub 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/torch-rechub — 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