RRepoGEO

REPOGEO REPORT · LITE

PaddlePaddle/PaddleRec

Default branch master · commit 656326ad · scanned 6/28/2026, 10:31:48 PM

GitHub: 4,080 stars · 653 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 PaddlePaddle/PaddleRec, 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
    Add broader category topics to improve AI categorization

    Why:

    CURRENT
    deepfm, esmm, gru4rec, lr, mmoe, ple, tdm, widedeep, word2vec
    COPY-PASTE FIX
    deepfm, esmm, gru4rec, lr, mmoe, ple, tdm, widedeep, word2vec, recommendation-system, deep-learning, machine-learning, recommender-systems, paddlepaddle, ai, algorithms, large-scale
  • highreadme#2
    Reposition the 'What is PaddleRec?' section to the top of the README

    Why:

    CURRENT
    The README starts with '最新动态' (Latest News) and '什么是推荐系统?' (What is a recommendation system?) sections.
    COPY-PASTE FIX
    Move the content of the '什么是PaddleRec?' section to immediately follow the language links at the very top of the README.
  • mediumabout#3
    Update repository description to highlight its 'all-in-one' nature and PaddlePaddle origin

    Why:

    CURRENT
    Recommendation Algorithm大规模推荐算法库，包含推荐系统经典及最新算法LR、Wide&Deep、DSSM、TDM、MIND、Word2Vec、Bert4Rec、DeepWalk、SSR、AITM，DSIN，SIGN，IPREC、GRU4Rec、Youtube_dnn、NCF、GNN、FM、FFM、DeepFM、DCN、DIN、DIEN、DLRM、MMOE、PLE、ESMM、ESCMM, MAML、xDeepFM、DeepFEFM、NFM、AFM、RALM、DMR、GateNet、NAML、DIFM、Deep Crossing、PNN、BST、AutoInt、FGCNN、FLEN、Fibinet、ListWise、DeepRec、ENSFM，TiSAS，AutoFIS等，包含经典推荐系统数据集criteo 、movielens等
    COPY-PASTE FIX
    PaddleRec: An all-in-one, large-scale recommendation algorithm library from the PaddlePaddle ecosystem, offering a full-process solution for developers and researchers. Includes classic and state-of-the-art models like LR, Wide&Deep, DSSM, TDM, and more.

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 PaddlePaddle/PaddleRec
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
tensorflow/recommenders
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. tensorflow/recommenders · recommended 1×
  2. pyg-team/pytorch_geometric · recommended 1×
  3. lyst/lightfm · recommended 1×
  4. NicolasHug/Surprise · recommended 1×
  5. RUCAIBox/RecBole · recommended 1×
  • CATEGORY QUERY
    What open-source libraries are available for building large-scale recommendation systems with deep learning models?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Recommenders (TFRS) (tensorflow/recommenders)
    2. PyTorch-Geometric (PyG) (pyg-team/pytorch_geometric)
    3. LightFM (lyst/lightfm)
    4. Surprise (NicolasHug/Surprise)
    5. RecBole (RUCAIBox/RecBole)
    6. DeepCTR (shenweichen/DeepCTR)

    AI recommended 6 alternatives but never named PaddlePaddle/PaddleRec. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a comprehensive toolkit for implementing various recommendation algorithms, including multi-task learning and ranking models.
    you: not recommended
    AI recommended (in order):
    1. RecBole
    2. Surprise
    3. LightFM
    4. TensorFlow Recommenders (TFRS)
    5. PyTorch-Geometric (PyG)
    6. Deep Graph Library (DGL)

    AI recommended 6 alternatives but never named PaddlePaddle/PaddleRec. 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 PaddlePaddle/PaddleRec?
    pass
    AI named PaddlePaddle/PaddleRec explicitly

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

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

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

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PaddlePaddle/PaddleRec — 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