🧾 Project Prompt (Assignment)

Crea un sistema que recomiende elementos similares a partir de características

Your product needs recommendations. Build a simple recommender and prove it works offline.

Build a recommendation system that suggests items to a user based on past interactions.

Requirements

• Create/choose an interactions dataset: (user_id, item_id, rating or click, timestamp optional)
• Implement at least:
  • Popularity-based baseline
  • One personalized method (kNN CF, matrix factorization, etc.)
• Provide a function or endpoint: “recommend for user X”
• Evaluate with a basic metric (e.g., hit-rate@K, precision@K)

🛠 Tools (Tech Stack)

• Python
• pandas + numpy
• scikit-learn (optional)
• Surprise (optional)

💡 Implementation Hints

• Always compare against the baseline (popularity)
• Handle cold-start users (fallback to popular items)
• If you have timestamps, do a time-aware split
• Save processed datasets and recommendation outputs for inspection