recommender-system

Build a recommender system by loading user-item interaction data, training collaborative filtering or SVD models, and generating top-K recommendations. This Skill implements user-based CF, item-based CF, SVD, and hybrid algorithms to produce ranked suggestions tailored to individual users across e-commerce, gaming, and content platforms.

Measure recommendation quality using Precision@K, Recall@K, MAE, and RMSE. This Skill computes these standard offline metrics alongside cross-validation and ablation analyses to quantify algorithm performance, compare approaches, and validate whether recommendations meet business accuracy targets.

Yes. Train user-based CF, item-based CF, SVD, and hybrid models on identical data, then evaluate and visualize their performance side-by-side. This Skill supports algorithm comparison through offline evaluation and visual analysis to identify which method best suits your specific user-item interaction patterns.

Assess data sparsity and cold-start impact through data quality checks and profiling. This Skill analyzes interaction matrix density and considers cold-start implications when training models, helping teams understand performance trade-offs with new users or items lacking historical data.

Load user-item interactions and item metadata, then validate data quality and sparsity. This Skill handles data profiling and quality checks as prerequisites, ensuring clean, well-structured matrices before training collaborative filtering, SVD, or hybrid models.

Yes. After training models offline, generate top-K recommendations for target users and run real-time simulations. This Skill supports both offline evaluation pipelines and runtime recommendation generation to test strategies before production deployment.