RecBole

Health v2 · maintenance only

Adoption not part of the score

4541 stars · 747 forks observed · 2026-08-28

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

RecBole is a unified, comprehensive and efficient recommendation library built on Python and PyTorch for reproducing and developing recommendation algorithms. It implements 94 algorithms across general, sequential, context-aware, and knowledge-based recommendation, with support for 44 benchmark datasets and standardized evaluation protocols.

Use cases

• reproduce and benchmark recommendation algorithms
• train collaborative filtering models on user-item interaction data
• run sequential recommendation experiments with transformers and RNNs
• evaluate context-aware and CTR prediction models
• experiment with knowledge-graph-based recommenders
• compare recommenders under standard evaluation protocols

When to choose

• you need a unified PyTorch framework for recommender systems research
• you want ready implementations of many classic and deep learning recommendation models
• you need standardized datasets and evaluation protocols for fair comparison
• you want GPU-accelerated training of recommendation models

When to avoid

• you need a production recommendation serving system rather than a research library
• your project is not based on Python and PyTorch
• you need a simple plug-and-play recommender with minimal configuration
• you require a managed or hosted recommendation service

Facets

library · maturity stable

machine-learning deep-learning data-science benchmarking machine-learning deep-learning data-science python cross-platform recommender-systems collaborative-filtering ctr-prediction sequential-recommendation knowledge-graph pytorch graph-neural-networks research algorithms gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="RUCAIBox/RecBole")