RRepoGEO

REPOGEO REPORT · LITE

RUCAIBox/CRSLab

Default branch main · commit 64979389 · scanned 6/13/2026, 10:21:43 AM

GitHub: 557 stars · 119 forks

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 RUCAIBox/CRSLab, 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 opening to clarify unique niche

    Why:

    CURRENT
    **CRSLab** is an open-source toolkit for building Conversational Recommender System (CRS).
    COPY-PASTE FIX
    **CRSLab** is the leading open-source toolkit specifically designed for building and benchmarking **Conversational Recommender Systems (CRS)**. Unlike general recommendation libraries or conversational AI frameworks, CRSLab focuses exclusively on the unique challenges and opportunities at their intersection, providing comprehensive models, datasets, and evaluation protocols.
  • mediumreadme#2
    Add a 'Comparison with other toolkits' section to README

    Why:

    COPY-PASTE FIX
    ## Comparison with other toolkits

While general recommendation libraries like RecBole or Surprise focus on traditional recommendation tasks, and conversational AI frameworks such as Rasa or Haystack provide tools for dialogue management, CRSLab uniquely specializes in the intersection: Conversational Recommender Systems. We offer dedicated benchmarks, models, and evaluation for scenarios where recommendation is integrated into a dialogue.
  • lowtopics#3
    Prioritize specific topics for conversational recommendation

    Why:

    CURRENT
    conversation-system, conversational-recommendation, deep-learning, dialog-system, graph-neural-network, human-machine-interaction, knowledge-graph, pretrained-models, pytorch, recommendation, recommender-system, text-generation
    COPY-PASTE FIX
    conversational-recommendation, recommender-system, conversation-system, dialog-system, deep-learning, graph-neural-network, human-machine-interaction, knowledge-graph, pretrained-models, pytorch, recommendation, text-generation

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 RUCAIBox/CRSLab
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Surprise
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Surprise · recommended 2×
  2. Rasa Open Source · recommended 1×
  3. Haystack · recommended 1×
  4. LightFM · recommended 1×
  5. spaCy · recommended 1×
  • CATEGORY QUERY
    What open-source toolkits are available for developing conversational AI recommendation systems in Python?
    you: not recommended
    AI recommended (in order):
    1. Rasa Open Source
    2. Haystack
    3. Surprise
    4. LightFM
    5. spaCy
    6. NLTK

    AI recommended 6 alternatives but never named RUCAIBox/CRSLab. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a comprehensive platform to benchmark and evaluate deep learning models for conversational recommendations.
    you: not recommended
    AI recommended (in order):
    1. RecBole
    2. Cornac
    3. Surprise
    4. TensorFlow Recommenders
    5. PyTorch-Ignite
    6. PyTorch Lightning

    AI recommended 6 alternatives but never named RUCAIBox/CRSLab. 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 RUCAIBox/CRSLab?
    pass
    AI named RUCAIBox/CRSLab explicitly

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

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

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

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RUCAIBox/CRSLab — 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