RRepoGEO

REPOGEO REPORT · LITE

AkaliKong/MiniOneRec

Default branch main · commit 0c64b955 · scanned 5/25/2026, 8:18:40 PM

GitHub: 1,590 stars · 226 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 AkaliKong/MiniOneRec, 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
    Update the repository 'About' description

    Why:

    CURRENT
    Minimal reproduction of OneRec
    COPY-PASTE FIX
    MiniOneRec: The first fully open-source framework for scaling generative recommendation, providing an end-to-end workflow for SID construction, SFT, and RL.
  • mediumhomepage#2
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2510.24431
  • mediumreadme#3
    Add a 'Key Differentiators' section to the README

    Why:

    COPY-PASTE FIX
    ### Why MiniOneRec?
MiniOneRec stands out as a focused, unified, and end-to-end framework specifically designed for one-stage deep learning generative recommendation models. Unlike general-purpose ML libraries or broader research frameworks, MiniOneRec provides a streamlined workflow from SID construction to SFT and RL, making it ideal for researchers and practitioners focused on scaling generative recommenders.

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 AkaliKong/MiniOneRec
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI API
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 1×
  2. Google Cloud Vertex AI · recommended 1×
  3. huggingface/transformers · recommended 1×
  4. langchain-ai/langchain · recommended 1×
  5. run-llama/llama_index · recommended 1×
  • CATEGORY QUERY
    How can I implement a generative recommendation system using large language models?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Google Cloud Vertex AI
    3. Hugging Face Transformers (huggingface/transformers)
    4. LangChain (langchain-ai/langchain)
    5. LlamaIndex (run-llama/llama_index)
    6. Cohere
    7. Azure OpenAI Service

    AI recommended 7 alternatives but never named AkaliKong/MiniOneRec. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source frameworks provide an end-to-end workflow for scaling generative recommenders?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Accelerate
    3. Hugging Face Optimum
    4. PyTorch Lightning
    5. DeepSpeed
    6. FairScale
    7. Ray
    8. PyTorch
    9. TensorFlow
    10. TensorFlow Extended (TFX)
    11. Merlin (NVIDIA)
    12. NVTabular
    13. HugeCTR

    AI recommended 13 alternatives but never named AkaliKong/MiniOneRec. 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 AkaliKong/MiniOneRec?
    pass
    AI named AkaliKong/MiniOneRec explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of AkaliKong/MiniOneRec. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/AkaliKong/MiniOneRec.svg)](https://repogeo.com/en/r/AkaliKong/MiniOneRec)
HTML
<a href="https://repogeo.com/en/r/AkaliKong/MiniOneRec"><img src="https://repogeo.com/badge/AkaliKong/MiniOneRec.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

AkaliKong/MiniOneRec — 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