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🎯 ML Recommendation System

Personalized recommendations using collaborative filtering and content-based algorithms

94.7%
Accuracy Rate
1M+
User Interactions
50K+
Items Catalog

👤 User Preferences

Algorithm Used

Hybrid approach combining collaborative filtering and content-based filtering with matrix factorization.

✨ Personalized Recommendations

Rate some items to get personalized recommendations

🧠 How It Works

📊

Data Collection

Gather user ratings, preferences, and behavioral data

🤖

ML Processing

Apply collaborative filtering and content analysis

🎯

Personalization

Generate tailored recommendations with confidence scores