RRepoGEO

REPOGEO REPORT · LITE

wzhe06/Reco-papers

Default branch master · commit 2c617a16 · scanned 6/19/2026, 12:07:59 PM

GitHub: 3,556 stars · 813 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
33 /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
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 wzhe06/Reco-papers, 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 opening to highlight curated collection with code links

    Why:

    CURRENT
    推荐系统论文、学习资料、业界分享 动态更新工作中实现或者阅读过的推荐系统相关论文、学习资料和业界分享，作为自己工作的总结，也希望能为推荐系统相关行业的同学带来便利。
    COPY-PASTE FIX
    精选并动态更新的经典与现代推荐系统论文、学习资料和业界分享，通常包含代码实现链接。本仓库旨在作为个人工作总结，并为推荐系统领域的学者、学生和从业者提供便利。
  • mediumtopics#2
    Add topics to clarify content type and unique value

    Why:

    CURRENT
    deep-learning, exploration-exploitation, machine-learning, recommendation, recommender-system, reinforcement-learning
    COPY-PASTE FIX
    deep-learning, exploration-exploitation, machine-learning, recommendation, recommender-system, reinforcement-learning, research-papers, paper-collection, code-implementations
  • lowreadme#3
    Explicitly mention inclusion of code links in README

    Why:

    COPY-PASTE FIX
    许多列出的论文也包含其对应的代码实现链接（通常在GitHub或其他平台），为实际应用提供实用资源。

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 wzhe06/Reco-papers
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ACM RecSys Proceedings
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. ACM RecSys Proceedings · recommended 1×
  2. ACM Digital Library · recommended 1×
  3. arXiv · recommended 1×
  4. Google Scholar · recommended 1×
  5. Coursera · recommended 1×
  • CATEGORY QUERY
    Where can I find academic papers and learning materials on building modern recommender systems?
    you: not recommended
    AI recommended (in order):
    1. ACM RecSys Proceedings
    2. ACM Digital Library
    3. arXiv
    4. Google Scholar
    5. Coursera
    6. edX
    7. University of Minnesota's specialization
    8. Netflix TechBlog
    9. Medium
    10. Towards Data Science

    AI recommended 10 alternatives but never named wzhe06/Reco-papers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best deep learning resources for improving personalized item recommendation and ranking?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Recommenders (tensorflow/recommenders)
    2. PyTorch-Geometric (pyg-team/pytorch_geometric)
    3. Deep Graph Library (dmlc/dgl)
    4. LightFM (lyst/lightfm)
    5. RecBole (RUCAIBox/RecBole)
    6. Surprise (NicolasHug/Surprise)
    7. Hugging Face Transformers (huggingface/transformers)
    8. DeepCTR (shenweichen/DeepCTR)

    AI recommended 8 alternatives but never named wzhe06/Reco-papers. 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 wzhe06/Reco-papers?
    pass
    AI named wzhe06/Reco-papers explicitly

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

  • If a team adopts wzhe06/Reco-papers in production, what risks or prerequisites should they evaluate first?
    pass
    AI named wzhe06/Reco-papers 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 wzhe06/Reco-papers solve, and who is the primary audience?
    pass
    AI did not name wzhe06/Reco-papers — 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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wzhe06/Reco-papers — 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