Recommendation Systems та рекомендаційні системи

🎓 Approaches

Collaborative Filtering

User-based: Recommendations based on similar users.

Item-based: Recommendations based on similar items.

Matrix Factorization: SVD, NMF for latent factors.

Content-Based

Concept: Recommendations based on item features.

Process: User profile, item features, matching.

Advantages: Works well for new items.

🔧 Deep Learning

Neural Collaborative Filtering

Concept: Neural networks for collaborative filtering.

Advantages: Non-linear interactions, complex patterns.

Applications: Modern recommendation systems.

Wide & Deep

Concept: Combination of wide (memorization) and deep (generalization).

Advantages: Balance between memorization and generalization.

Applications: Google Play, production systems.

DeepFM

Concept: Factorization Machines with deep learning.

Advantages: Automatic feature interactions.

Applications: CTR prediction, recommendations.

📚 Practical Examples

Example 1: Matrix Factorization for Recommendations

Matrix: Create a user-item matrix.

Factorization: Decompose it into latent factors (SVD).

Prediction: Predict ratings through factors.

Recommendation: Recommend top items.

Example 2: Neural Collaborative Filtering

Embeddings: Create embeddings for users and items.

Neural Network: Train NN for prediction.

Recommendation: Recommend based on predictions.

Try it live

Everything above runs in your browser — open Recommendation System Bipartite Graph and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.