Recommendation Systems (Collaborative Filtering)

Architecture Overview

graph TD
    A[User-Item Interactions] --> B[Data Preprocessing]
    B --> C[Collaborative Filtering Model]
    C --> D{Model Type}
    D --> E[Matrix Factorization]
    D --> F[Neighborhood Methods]
    E --> G[Recommendations]
    F --> G

Best Practices

• Data Sparsity: Handle cold-start problems with hybrid approaches or content-based fallbacks.
• Evaluation: Use metrics like NDCG, Precision@K, and Recall@K for ranking quality. Avoid relying solely on RMSE.