alibaba/EasyRec

Health v2 · maintenance only

Adoption not part of the score

2356 stars · 384 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

EasyRec is a TensorFlow-based framework from Alibaba for building large-scale deep learning recommendation models covering matching, ranking, and multi-task learning. It provides configuration-driven model building, hyperparameter tuning, large-scale embedding, online learning, and deployment support across local and Alibaba Cloud platforms.

Use cases

• train ctr prediction models like deepfm and wide-and-deep
• build two-tower matching models for candidate generation
• run multi-task learning for recommendation ranking
• do online learning for recommender systems at scale
• hyperparameter search for recommendation models
• deploy large-scale embedding recommendation models

When to choose

• you need production-scale deep learning recommendation models with config-driven workflows
• you use Alibaba Cloud data platforms like MaxCompute, EMR, or PAI
• you want built-in HPO, knowledge distillation, and online learning for recsys

When to avoid

• you prefer PyTorch - consider TorchEasyRec instead
• you need a general-purpose ML framework outside recommendation
• you want a lightweight library without cloud platform integration

Facets

framework · maturity active

machine-learning deep-learning llm-training data-science machine-learning large-language-models data-science e-commerce python cloud recommender-system ctr-prediction deepfm dssm multi-task-learning online-learning tensorflow automl candidate-generation ranking linux docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="alibaba/EasyRec")