REPOGEO REPORT · LITE

benedekrozemberczki/awesome-gradient-boosting-papers

Default branch master · commit 189d4f57 · scanned 5/14/2026, 8:26:42 AM

GitHub: 1,050 stars · 166 forks

AI VISIBILITY SCORE

22 /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

1 / 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 benedekrozemberczki/awesome-gradient-boosting-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

Strengthen README's opening value proposition

Why:

CURRENT

A curated list of gradient and adaptive boosting papers with implementations from the following conferences:

COPY-PASTE FIX

This is a curated, human-filtered list of cutting-edge gradient and adaptive boosting research papers, complete with implementations. Unlike general search engines or broader academic databases, this repository provides a focused, organized collection to save machine learning researchers and data scientists time in discovering key advancements and their practical applications.

mediumabout#2
Add repository URL to homepage metadata
Why:
COPY-PASTE FIX
```
https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers
```

lowreadme#3

Clarify target audience's intent in README

Why:

COPY-PASTE FIX

This collection is ideal for those looking to understand the theoretical underpinnings and detailed implementations of gradient boosting, rather than just seeking to apply pre-built libraries.

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 benedekrozemberczki/awesome-gradient-boosting-papers

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

arXiv.org

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

arXiv.org · recommended 1×
Google Scholar · recommended 1×
GitHub · recommended 1×
Papers With Code · recommended 1×
NeurIPS · recommended 1×

CATEGORY QUERY
Where can I find recent research papers and implementations on gradient boosting algorithms?
you: not recommended
AI recommended (in order):
1. arXiv.org
2. Google Scholar
3. GitHub
4. Papers With Code
5. NeurIPS
6. ICML
7. ICLR
8. KDD
9. Towards Data Science
10. Medium
11. XGBoost (dmlc/xgboost)
12. LightGBM (microsoft/LightGBM)
13. CatBoost (catboost/catboost)
14. Scikit-learn (scikit-learn/scikit-learn)
AI recommended 14 alternatives but never named benedekrozemberczki/awesome-gradient-boosting-papers. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the most effective boosting techniques for improving classification model performance?
you: not recommended
AI recommended (in order):
1. XGBoost
2. LightGBM
3. CatBoost
4. AdaBoost
5. Gradient Boosting Machines
AI recommended 5 alternatives but never named benedekrozemberczki/awesome-gradient-boosting-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
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 benedekrozemberczki/awesome-gradient-boosting-papers?
pass
AI did not name benedekrozemberczki/awesome-gradient-boosting-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?
If a team adopts benedekrozemberczki/awesome-gradient-boosting-papers in production, what risks or prerequisites should they evaluate first?
pass
AI named benedekrozemberczki/awesome-gradient-boosting-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 benedekrozemberczki/awesome-gradient-boosting-papers solve, and who is the primary audience?
pass
AI did not name benedekrozemberczki/awesome-gradient-boosting-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?

Embed your GEO score

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MARKDOWN (README)

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benedekrozemberczki/awesome-gradient-boosting-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