Building Recommendation Systems with AI
Recommendation engines drive personalization on platforms like Netflix, Amazon, and YouTube. Here's how to build your own using modern AI techniques.
Types of Recommendation Systems
Collaborative Filtering
Recommend based on user behavior similarity. "Users who liked X also liked Y." The most common approach.
Content-Based Filtering
Recommend based on item features. "This movie has similar actors/director/genre to movies you liked."
Hybrid Approaches
Combine multiple techniques for better recommendations. Most production systems use hybrid approaches.
Modern AI Approaches
- Embedding-based: Use neural networks to learn user and item embeddings
- Sequence models: Use transformers to predict what users want next
- LLM-powered: Use large language models to understand user intent and preferences
Implementation Stack
- Data Storage: PostgreSQL or MongoDB
- Feature Store: Redis or Feast
- Model Serving: TensorFlow Serving or ONNX Runtime
- Vector Search: Pinecone or Weaviate for similarity search