Rec System
C 70 completed
Other
unknown / markdown · small
122
Files
105,169
LOC
2
Frameworks
6
Languages
Pipeline State
completedRun ID
#345150Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
CatalogedDecision
proceedNovelty
58.13Framework unique
—Isolation
—Last stage change
2026-05-10 03:34:57Deduplication group #48903
Member of a group with 15 similar repo(s) — this repo is canonical view group →
Top concepts (2)
Project DescriptionWeb Backend
Generated by Repobility's multi-pass static-analysis pipeline (https://repobility.com)
AI Prompt
Create a comprehensive learning resource for recommendation systems, similar to what's found in the repo. I need it to cover topics from CTR prediction to generative recommendation, drawing inspiration from industry leaders like Meta and Alibaba. The structure should be modular, perhaps using FastAPI for any potential API endpoints, and I'd like to include runnable Jupyter notebooks for hands-on exercises across different parts of the curriculum, such as Foundations, CTR Prediction, and Generative RecSys. Please ensure the documentation is well-structured, perhaps using MkDocs, and include setup instructions using a Makefile.
recommendation-systems machine-learning fastapi jupyter notebooks curriculum documentation ai deep-learning
Generated by gemma4:latest
Catalog Information
A comprehensive learning resource covering CTR prediction, generative recommendation, and modern industrial systems from Meta, Tencent, ByteDance, and Alibaba.
Description
A comprehensive learning resource covering CTR prediction, generative recommendation, and modern industrial systems from Meta, Tencent, ByteDance, and Alibaba.
Novelty
3/10Tags
recommendation-systems machine-learning fastapi jupyter notebooks curriculum documentation ai deep-learning
Technologies
fastapi pydantic
Claude Models
claude-opus-4-6
Quality Score
C
69.9/100
Structure
62
Code Quality
100
Documentation
57
Testing
15
Practices
78
Security
100
Dependencies
50
Strengths
- CI/CD pipeline configured (github_actions)
- Code linting configured (ruff (possible))
- Consistent naming conventions (snake_case)
- Good security practices \u2014 no major issues detected
- Properly licensed project
Weaknesses
- No tests found \u2014 high risk of regressions
Recommendations
- Add a test suite \u2014 start with critical path integration tests
Security & Health
4.1h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (0)
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MIT
License
0.0%
Duplication
Languages
Frameworks
FastAPI pytest
Concepts (2)
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| auto_description | Project Description | A comprehensive learning resource covering CTR prediction, generative recommendation, and modern industrial systems from Meta, Tencent, ByteDance, and Alibaba. | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_category | Web Backend | web-backend | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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