recommender-system

What problem does it solve?

This Skill provides a complete end-to-end recommender system analysis framework, implementing multiple recommendation algorithms, evaluation methods, and visualization tools to help teams deliver personalized recommendations, measure performance, and optimize ROI.

Core Features & Use Cases

• Multiple Algorithms: User-based CF, item-based CF, SVD, and hybrid methods.
• Offline Evaluation: Precision@K, Recall@K, MAE, RMSE, cross-validation, and ablation analyses.
• Visualization & Insights: Recommendation results, algorithm comparisons, and user/item analytics.
• Data Quality & Profiling: Data quality checks, sparsity analysis, and cold-start considerations.

Quick Start

Quickly set up datasets, train models, generate top-K recommendations for a user, and evaluate performance. Steps:

• Load user-item interactions and item metadata
• Train user-based CF, item-based CF, and SVD
• Generate hybrid recommendations for a target user and visualize results