Recommendation System with Neural Networks

Create a neural network-based recommendation system.

System requirements:
- Recommendation type: [COLLABORATIVE/CONTENT/HYBRID]
- Items to recommend: [PRODUCTS/MOVIES/MUSIC/etc.]
- User interactions: [RATINGS/CLICKS/PURCHASES]
- Scale: [NUMBER OF USERS AND ITEMS]

Components needed:
1. Data preprocessing:
   - User-item interaction matrix
   - Negative sampling
   - Train/test splitting (temporal/random)
2. Model architectures:
   - Neural Collaborative Filtering (NCF)
   - Matrix Factorization with embeddings
   - Two-tower models
   - Graph Neural Networks (optional)
3. Feature engineering:
   - User features
   - Item features
   - Contextual features
4. Training:
   - BPR/BCE loss
   - Contrastive learning
   - Hard negative mining
5. Evaluation:
   - Hit Rate, NDCG, MRR
   - Offline/online metrics
   - A/B testing framework
6. Serving:
   - Approximate nearest neighbors
   - Real-time inference
   - Cold start handling

Balance between relevance and diversity.