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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 generalize to new items (via item features) and to new users (via user features).
Features
- Install from pip
- Documentation available
- Examples available
- A Python implementation of LightFM
- Hybrid recommendation algorithm
- Articles and tutorials on using LightFM
Project Samples
Categories
Machine LearningLicense
Apache License V2.0Follow LightFM
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