lyst/lightfm

Health v2 · maintenance only

Adoption not part of the score

5111 stars · 725 forks observed · 2026-08-28

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

LightFM is a Python library implementing hybrid recommendation algorithms that combine collaborative filtering with user and item metadata via matrix factorization. It supports implicit and explicit feedback with efficient BPR and WARP ranking losses and multithreaded training.

Use cases

• build a movie recommendation engine in python
• recommend items to users with implicit feedback data
• handle cold-start recommendations using item and user metadata
• train a matrix factorization model with WARP ranking loss
• build a hybrid recommender combining content features and collaborative signals
• evaluate recommender models with precision at k

When to choose

• you need hybrid recommendations that generalize to new users and items via metadata
• you want fast, multithreaded matrix factorization with ranking losses like BPR or WARP
• you work in Python with sparse interaction data and want a simple, well-proven API

When to avoid

• you need deep learning or sequence-based recommenders rather than factorization models
• you need a scalable distributed recommender for very large industrial datasets
• you need actively developed features; the project is largely in maintenance mode

Facets

library · maturity maintenance

machine-learning search-engine machine-learning data-science analytics python windows recommender-system matrix-factorization collaborative-filtering hybrid-recommendations learning-to-rank implicit-feedback bpr warp linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="lyst/lightfm")