Recommendation Systems

Recommendation Systems (Collaborative Filtering)

Architecture Overview

%%{init: {"theme": "default", "flowchart": {"useMaxWidth": true}}}%%
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.

Code Snippet: Matrix Factorization (Surprise Library)

from surprise import Dataset, Reader, SVD
from surprise.model_selection import cross_validate

# Load data
data = Dataset.load_builtin('ml-100k')

# Use Singular Value Decomposition (Matrix Factorization)
algo = SVD()

# Cross-validation
results = cross_validate(algo, data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
print(f"Mean RMSE: {results['test_rmse'].mean()}")