Microsoft Recommenders is an open-source collection of best practices, examples, and utilities for building recommendation systems. It covers the full recommendation workflow, from data preparation and baseline methods to modern machine-learning algorithms, evaluation, tuning, and deployment-oriented experimentation.

The repository includes notebooks and reference implementations for collaborative filtering, content-based approaches, deep learning, sequential recommendation, and other techniques, together with common datasets and evaluation metrics. It is designed as a practical resource for comparing methods under consistent workflows rather than as one monolithic recommender product.

Recommenders is useful for data scientists and engineers developing personalization, ranking, and recommendation features. The examples can serve as educational material, experiment baselines, or starting points for production systems where teams need to choose an algorithm based on their data and business constraints.