recommendation-ml

Features and purpose

ML recommendation system development with collaborative filtering (Matrix Factorization), content-based filtering, and hybrid approaches. Use when building recommendation models, implementing Feast feature stores, setting up MLflow model registry, handling cold-start problems for new users/products, implementing diversity with MMR algorithm, or adding exploration with Thompson Sampling/epsilon-greedy bandits.

Quick start

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.