RRepoGEO

REPOGEO REPORT · LITE

bytedance/HLLM

Default branch main · commit 864f1722 · scanned 6/2/2026, 6:53:33 PM

GitHub: 621 stars · 82 forks

AI VISIBILITY SCORE
28 /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
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 bytedance/HLLM, 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
    Add a concise introductory paragraph to the README

    Why:

    COPY-PASTE FIX
    # HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling
    
    # HLLM-Creator: Hierarchical LLM-based Personalized Creative Generation
    
    This repository presents HLLM, a novel framework leveraging hierarchical large language models to significantly enhance sequential recommendation systems through advanced item and user modeling. Additionally, HLLM-Creator extends this approach to personalized creative content generation, enabling highly relevant and engaging outputs based on detailed user behavior. Both projects aim to push the boundaries of LLM applications in personalized intelligence.
    
    <div align="left">
  • hightopics#2
    Add specific, descriptive topics to improve categorization

    Why:

    CURRENT
    research
    COPY-PASTE FIX
    sequential-recommendation, large-language-models, llm-applications, personalized-recommendation, creative-generation, user-modeling, deep-learning-research, recommendation-systems
  • mediumhomepage#3
    Add the primary arXiv paper link as the repository homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2409.12740

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 bytedance/HLLM
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. BERT · recommended 1×
  3. GPT-2 · recommended 1×
  4. RoBERTa · recommended 1×
  5. T5 · recommended 1×
  • CATEGORY QUERY
    How to improve sequential recommendation systems using hierarchical large language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. BERT
    3. GPT-2
    4. RoBERTa
    5. T5
    6. BERT4Rec
    7. DeepMind's Perceiver IO
    8. Perceiver Autoregressive
    9. Google's Reformer
    10. Longformer
    11. Microsoft's DeBERTa
    12. ELECTRA
    13. OpenAI's GPT-3.5
    14. GPT-4
    15. Graph Neural Networks
    16. PyTorch Geometric
    17. GraphSAGE
    18. GAT
    19. LightGCN

    AI recommended 19 alternatives but never named bytedance/HLLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking tools for personalized creative content generation based on user behavior modeling.
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Hugging Face Transformers & Diffusers
    3. Phrasee
    4. Persado
    5. Canva
    6. Jasper

    AI recommended 6 alternatives but never named bytedance/HLLM. 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 bytedance/HLLM?
    pass
    AI named bytedance/HLLM explicitly

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

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

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

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bytedance/HLLM — 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