# Recommendation Systems

> Implementation of Collaborative Filtering for recommendation systems.

- Skill: `j4flmao/recommendation-systems` (Agent Skill)
- Install (CLI): `npx skillmds@latest add j4flmao/recommendation-systems`
- Raw SKILL.md: https://api.skillmd.com/api/skills/j4flmao/recommendation-systems/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: j4flmao (https://skillmd.com/u/j4flmao)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/j4flmao/recommendation-systems

---


# Recommendation Systems (Collaborative Filtering)

## Architecture Overview

```mermaid
%%{init: {"theme": "default", "flowchart": {"useMaxWidth": true}}}%%
graph TD
    A[User-Item Interactions] --> B[Data Preprocessing]
    B --> C[Collaborative Filtering Model]
    C --> D{Model Type}
    D --> E[Matrix Factorization]
    D --> F[Neighborhood Methods]
    E --> G[Recommendations]
    F --> G
```

## Best Practices
- **Data Sparsity**: Handle cold-start problems with hybrid approaches or content-based fallbacks.
- **Evaluation**: Use metrics like NDCG, Precision@K, and Recall@K for ranking quality. Avoid relying solely on RMSE.

## Code Snippet: Matrix Factorization (Surprise Library)
```python
from surprise import Dataset, Reader, SVD
from surprise.model_selection import cross_validate

# Load data
data = Dataset.load_builtin('ml-100k')

# Use Singular Value Decomposition (Matrix Factorization)
algo = SVD()

# Cross-validation
results = cross_validate(algo, data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
print(f"Mean RMSE: {results['test_rmse'].mean()}")
```