RRepoGEO

REPOGEO REPORT · LITE

DeepRec-AI/DeepRec

Default branch main · commit d1c5a6e9 · scanned 5/14/2026, 11:41:45 PM

GitHub: 1,182 stars · 361 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 DeepRec-AI/DeepRec, 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 introduction to emphasize core capabilities

    Why:

    CURRENT
    DeepRec is a high-performance recommendation deep learning framework based on TensorFlow 1.15, Intel-TensorFlow and NVIDIA-TensorFlow. It is hosted in incubation in LF AI & Data Foundation.
    COPY-PASTE FIX
    DeepRec is a high-performance, large-scale deep learning framework specifically designed for recommendation systems, enabling distributed training of models with massive parameters. Hosted in incubation in LF AI & Data Foundation, it builds upon TensorFlow 1.15, Intel-TensorFlow, and NVIDIA-TensorFlow.
  • mediumhomepage#2
    Add official project homepage URL

    Why:

    COPY-PASTE FIX
    [Your project's official homepage URL here]
  • lowreadme#3
    Add a 'Why DeepRec?' or 'Key Differentiators' section

    Why:

    COPY-PASTE FIX
    Add a new section (e.g., 'Why DeepRec?' or 'Key Differentiators') to the README. Explicitly state its origin as an industrial-scale system from Alibaba and its specialized optimizations for extreme scale, performance, and sparse data challenges in recommendation systems.

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 DeepRec-AI/DeepRec
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 4 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 4×
  2. apache/spark · recommended 3×
  3. tensorflow/recommenders · recommended 2×
  4. Lightning-AI/lightning · recommended 2×
  5. lyst/lightfm · recommended 1×
  • CATEGORY QUERY
    What are the best frameworks for building high-performance, large-scale recommendation systems?
    you: not recommended
    AI recommended (in order):
    1. Apache Spark MLlib (apache/spark)
    2. TensorFlow Recommenders (tensorflow/recommenders)
    3. PyTorch-Lightning (Lightning-AI/lightning)
    4. LightFM (lyst/lightfm)
    5. Surprise (NicolasHug/Surprise)
    6. RecBole (RUCAIBox/RecBole)

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

    Show full AI answer
  • CATEGORY QUERY
    How to scale deep learning models for recommendations with massive parameters and distributed training?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow (tensorflow/tensorflow)
    2. TensorFlow Extended (TFX) (tensorflow/tfx)
    3. TensorFlow Recommenders (TFRs) (tensorflow/recommenders)
    4. Google Cloud AI Platform
    5. Vertex AI
    6. PyTorch (pytorch/pytorch)
    7. PyTorch Lightning (Lightning-AI/lightning)
    8. DeepSpeed (microsoft/DeepSpeed)
    9. FairScale (facebookresearch/fairscale)
    10. AWS SageMaker
    11. Azure Machine Learning
    12. NVIDIA Merlin (NVIDIA/Merlin)
    13. HugeCTR (NVIDIA/HugeCTR)
    14. NVTabular (NVIDIA/NVTabular)
    15. Triton Inference Server (triton-inference-server/server)
    16. Ray (ray-project/ray)
    17. Ray Train (ray-project/ray)
    18. Ray Core (ray-project/ray)
    19. Ray Data (ray-project/ray)
    20. Apache Spark (apache/spark)
    21. Spark MLlib (apache/spark)
    22. Horovod (horovod/horovod)
    23. Metaflow (Netflix/metaflow)
    24. AWS Batch
    25. Kubernetes (kubernetes/kubernetes)

    AI recommended 25 alternatives but never named DeepRec-AI/DeepRec. 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 DeepRec-AI/DeepRec?
    pass
    AI named DeepRec-AI/DeepRec explicitly

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

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

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

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DeepRec-AI/DeepRec — 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