Train personalized recommendation model

Develop and train ML model for content recommendations.

Model Architecture:

• Collaborative filtering (user-item interactions)
• Content-based filtering (item features)
• Hybrid approach combining both

Features:

• User viewing history
• Watch time duration
• Ratings and likes
• Content metadata (genre, tags, actors)
• User demographics
• Time of day patterns

Implementation:

• Data collection pipeline
• Feature engineering
• Model selection (Matrix Factorization, Neural CF, Transformers)
• Training pipeline with MLflow
• Model evaluation (precision@k, recall@k, NDCG)
• A/B testing framework
• Serving infrastructure

Tech Stack: Python, PyTorch/TensorFlow, MLflow, Ray Dataset Size: 10M+ interactions