recommenders-team/recommenders

Health v2 · maintenance only

Adoption not part of the score

21864 stars · 3319 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A Python library and collection of Jupyter notebooks with best practices for building, evaluating, and operationalizing recommendation systems, from classic algorithms like ALS to deep learning models like xDeepFM. It is a Linux Foundation AI & Data project providing utilities for data preparation, model selection, offline evaluation, and production deployment.

Use cases

• build a recommendation system in python
• compare recommender algorithms like ALS and xDeepFM
• evaluate recommendation models with offline metrics
• tune hyperparameters for a recommendation model
• deploy a recommendation model to production
• learn best practices for recommender systems
• split and prepare data for recommendation algorithms

When to choose

• you want ready-made implementations and notebooks for classic and deep learning recommenders
• you need utilities for dataset loading, train/test splitting, and offline evaluation of recommendations
• you want to prototype and operationalize recommendation models on Azure or Kubernetes

When to avoid

• you need a turnkey production recommendation service with no ML coding
• your use case is general machine learning unrelated to recommendations
• you require a framework outside the Python/Spark ecosystem

Facets

library · maturity active

machine-learning data-science benchmarking etl machine-learning data-science artificial-intelligence tutorials python cloud recommender-systems jupyter-notebooks collaborative-filtering deep-learning-recommendation model-evaluation hyperparameter-tuning gpu docker kubernetes

Member repositories

For agents

markdown · JSON · MCP: product_card(name="recommenders-team/recommenders")