recommendation-ml
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.
Installation and usage
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.
Después de instalarlo, puedes usar este skill ejecutando el siguiente comando en tu terminal:
skills use recommendation-ml