recbole
A unified, comprehensive and efficient recommendation library
Decision gist · record as of 2026-08-14
Yes, if you are conducting recommendation research and need a unified benchmark framework. The library is actively maintained, permissively licensed, and has low install friction. However, it is research-focused and not designed for production systems; the aging maintenance status and large dependency footprint (16 runtime packages) mean you should verify that the algorithm coverage and dataset versions match your specific research needs before committing.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires PyTorch 1.7.0 or later; GPU use requires CUDA 9.2+ and NVIDIA driver >= 396.26 (Linux) or >= 397.44 (Windows 10).
- Low install friction with a pure-Python wheel.
- The package is actively maintained with a recent release (2025-02-24) and has accumulated 4526 repository stars, though maintenance status is marked as aging.
License · maintenance · safety
permissive license (permissive) — MIT license permits unrestricted use, modification, and distribution in both open-source and commercial contexts with minimal restrictions.
last release 2025-02-24 (536 days) · last repo commit 2025-02-24 · 4,526 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 90,548 downloads/mo, #13,581 on PyPI
Alternatives
Verify before relying
pip install recbole
from recbole.quick_start import quick_start
quick_start(model='BPR', dataset='ml-100k')- Actual number of implemented algorithms (description claims 94 but does not specify which are in v1.2.1)
- Whether all 44 benchmark datasets are included or require separate download
- Multi-GPU and mixed precision training support status in v1.2.1
- Python version requirement (description says 3.7+, but requires_python is unspecified)
What it is and what it does
RecBole is a research-focused recommendation library built on PyTorch that bundles a large collection of recommendation algorithms and preprocessed benchmark datasets into a single framework. It abstracts away data loading, evaluation protocols, and model training boilerplate so researchers can focus on algorithm development and comparison. The library covers four recommendation task categories: general (e.g., collaborative filtering), sequential (e.g., next-item prediction), context-aware (e.g., factorization machines), and knowledge-based (e.g., knowledge graph embeddings).
The package depends on 16 runtime libraries including torch, numpy, scipy, pandas, scikit-learn, and tensorboard. It is designed for GPU-accelerated training and includes utilities for hyperparameter tuning, significance testing, and standardized evaluation. The framework is intended for reproducible research rather than production deployment, with emphasis on benchmarking and comparing algorithms on common datasets.
Use it for
- Reproduce published recommendation algorithm results on standard benchmarks to verify claims or understand baselines.
- Implement and test a new recommendation algorithm within a unified data and evaluation framework.
- Compare multiple recommendation approaches on the same dataset using standardized metrics and train/test splits.
- Preprocess and format raw recommendation data into RecBole's unified structure for consistent downstream use.
- Tune hyperparameters across multiple recommendation models using built-in search and parallel evaluation.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are conducting recommendation research and need a unified benchmark framework.
The library is actively maintained, permissively licensed, and has low install friction. However, it is research-focused and not designed for production systems; the aging maintenance status and large dependency footprint (16 runtime packages) mean you should verify that the algorithm coverage and dataset versions match your specific research needs before committing.
Install
recbole on PyPI
Before you install
Low install friction with a pure-Python wheel. The package is actively maintained with a recent release (2025-02-24) and has accumulated 4526 repository stars, though maintenance status is marked as aging.
Requires PyTorch 1.7.0 or later; GPU use requires CUDA 9.2+ and NVIDIA driver >= 396.26 (Linux) or >= 397.44 (Windows 10).
License in practice
MIT license permits unrestricted use, modification, and distribution in both open-source and commercial contexts with minimal restrictions.
Quickstart
pip install recbole
from recbole.quick_start import quick_start
quick_start(model='BPR', dataset='ml-100k')Verify before relying
- Actual number of implemented algorithms (description claims 94 but does not specify which are in v1.2.1)
- Whether all 44 benchmark datasets are included or require separate download
- Multi-GPU and mixed precision training support status in v1.2.1
- Python version requirement (description says 3.7+, but requires_python is unspecified)
Package facts
| License | permissive license permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 16 packagestorchnumpyscipypandastqdmcolorlogcoloramascikit-learnpyyamltensorboardthoptabulateplotlytexttablepsutilray |
| Maintenance | Aging 536 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 90,548 / month, #13,581 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT License |
Evidence: recbole-1.2.1-py3-none-any.whl
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