A Python 3.12+ compatible fork of the original LightFM recommendation library.

Build status
Linux & macOS (3.8-3.12)

Note: This is a community-maintained fork that provides Python 3.12+ compatibility by fixing Cython 3.0+ build issues. All credit goes to the original LightFM authors. If you're using Python < 3.12, consider using the original LightFM package.

LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback, including efficient implementation of BPR and WARP ranking losses. It's easy to use, fast (via multithreaded model estimation), and produces high quality results.

It also makes it possible to incorporate both item and user metadata into the traditional matrix factorization algorithms. It represents each user and item as the sum of the latent representations of their features, thus allowing recommendations to generalise to new items (via item features) and to new users (via user features).

For more details, see the Documentation.

Need help? Contact me via email, Twitter, or Gitter.

Install lightfm-next from PyPI:

pip install lightfm-next

Note: Windows support is not available yet. Use Linux or macOS.

Use the original LightFM package:

pip install lightfm

or Conda:

conda install -c conda-forge lightfm

lightfm-next is a drop-in replacement for the original LightFM. Simply replace your installation:

# Replace this
pip uninstall lightfm
pip install lightfm-next

No code changes required - all imports and APIs remain identical:

from lightfm import LightFM  # Works exactly the same

Fitting an implicit feedback model on the MovieLens 100k dataset is very easy:

from lightfm import LightFM
from lightfm.datasets import fetch_movielens
from lightfm.evaluation import precision_at_k

# Load the MovieLens 100k dataset. Only five
# star ratings are treated as positive.
data = fetch_movielens(min_rating=5.0)

# Instantiate and train the model
model = LightFM(loss='warp')
model.fit(data['train'], epochs=30, num_threads=2)

# Evaluate the trained model
test_precision = precision_at_k(model, data['test'], k=5).mean()

1. Learning to Rank Sketchfab Models with LightFM
2. Metadata Embeddings for User and Item Cold-start Recommendations
3. Recommendation Systems - Learn Python for Data Science
4. Using LightFM to Recommend Projects to Consultants

Please cite LightFM if it helps your research. You can use the following BibTeX entry:

@inproceedings{DBLP:conf/recsys/Kula15,
  author    = {Maciej Kula},
  editor    = {Toine Bogers and
               Marijn Koolen},
  title     = {Metadata Embeddings for User and Item Cold-start Recommendations},
  booktitle = {Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender
               Systems co-located with 9th {ACM} Conference on Recommender Systems
               (RecSys 2015), Vienna, Austria, September 16-20, 2015.},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {1448},
  pages     = {14--21},
  publisher = {CEUR-WS.org},
  year      = {2015},
  url       = {http://ceur-ws.org/Vol-1448/paper4.pdf},
}

Pull requests are welcome. To install for development:

1. Clone the repository: git clone https://github.com/midodimori/lightfm-next.git
2. Install UV: curl -LsSf https://astral.sh/uv/install.sh | sh
3. Install dependencies: cd lightfm-next && uv sync --extra dev --extra lint
4. Run all tests and linting: make test-all

Available make commands:

• make install - Install dependencies and build extensions
• make lint - Run flake8 linting
• make test - Run pytest
• make test-basic - Run basic functionality test
• make test-all - Run complete test suite (same as CI)

When making changes to .pyx extension files, run uv run python setup.py build_ext --inplace to rebuild extensions.