RRepoGEO

REPOGEO REPORT · LITE

DeepRec-AI/DeepRec

Default branch main · commit d1c5a6e9 · scanned 6/25/2026, 2:02:31 PM

GitHub: 1,192 stars · 359 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)

3 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 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
  • highabout#1
    Enhance the 'About' description to highlight core differentiators

    Why:

    CURRENT
    DeepRec is a high-performance recommendation deep learning framework based on TensorFlow. It is hosted in incubation in LF AI & Data Foundation.
    COPY-PASTE FIX
    DeepRec is an industrial-grade, high-performance, large-scale, and distributed recommendation deep learning framework based on TensorFlow, hosted in incubation in LF AI & Data Foundation.
  • mediumtopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    advertising, deep-learning, distributed-training, machine-learning, python, recommendation-engine, scalability, search-engine
    COPY-PASTE FIX
    advertising, deep-learning, distributed-training, machine-learning, python, recommendation-engine, recommendation-systems, large-scale, distributed-deep-learning, scalability, search-engine
  • mediumreadme#3
    Refine the README's opening sentence to include key differentiators

    Why:

    CURRENT
    DeepRec is a high-performance recommendation deep learning framework based on TensorFlow 1.15, Intel-TensorFlow and NVIDIA-TensorFlow.
    COPY-PASTE FIX
    DeepRec is an industrial-grade, battle-tested, high-performance recommendation deep learning framework based on TensorFlow 1.15, Intel-TensorFlow and NVIDIA-TensorFlow.

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
tensorflow/tensorflow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. tensorflow/tensorflow · recommended 2×
  2. TensorFlow · recommended 1×
  3. TensorFlow Recommenders · recommended 1×
  4. PyTorch · recommended 1×
  5. PyTorch-Lightning · recommended 1×
  • CATEGORY QUERY
    What are the best frameworks for building high-performance, large-scale recommendation systems using deep learning?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow
    2. TensorFlow Recommenders
    3. PyTorch
    4. PyTorch-Lightning
    5. DeepCTR-Torch
    6. NVIDIA Merlin
    7. NVTabular
    8. HugeCTR
    9. Merlin Models
    10. DeepCTR
    11. LightFM
    12. Apache Spark MLlib
    13. Spark-TensorFlow Connector
    14. Horovod on Spark

    AI recommended 14 alternatives but never named DeepRec-AI/DeepRec. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to efficiently train massive deep learning models with distributed TensorFlow for advertising and search applications?
    you: not recommended
    AI recommended (in order):
    1. Horovod (horovod/horovod)
    2. tf.distribute.Strategy (tensorflow/tensorflow)
    3. tf.estimator (tensorflow/tensorflow)
    4. Ray Train (ray-project/ray)
    5. Kubeflow Training Operator (kubeflow/training-operator)
    6. DeepSpeed (microsoft/DeepSpeed)

    AI recommended 6 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