benfred/implicit

Health v2 · maintenance only

Adoption not part of the score

3815 stars · 630 forks observed · 2026-08-28

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

A fast Python library implementing collaborative filtering recommendation algorithms for implicit feedback datasets, including ALS, BPR, logistic matrix factorization, and item-item nearest neighbour models. It uses Cython/OpenMP for multi-threaded CPU training and custom CUDA kernels for GPU acceleration, with optional approximate nearest neighbour support via Annoy, NMSLIB, and Faiss.

Use cases

• build a recommender system from implicit user feedback like clicks or plays
• train an ALS matrix factorization model on a sparse user-item matrix
• find similar items in a catalog using collaborative filtering
• recommend products or music to users based on interaction history
• speed up recommendation inference with approximate nearest neighbours
• train recommendation models on GPU with CUDA

When to choose

• you have implicit feedback data (views, purchases, plays) rather than explicit ratings
• you need fast, multi-threaded or GPU-accelerated training of classic recommendation models
• you want a simple pip-installable Python library with a scikit-learn-like fit/recommend API

When to avoid

• you need explicit-rating-based collaborative filtering or deep learning recommenders
• you work outside Python or need a hosted recommendation service
• your dataset is tiny and a simple heuristic would suffice

Facets

library · maturity active

machine-learning data-science machine-learning data-science analytics python windows collaborative-filtering recommender-system matrix-factorization implicit-feedback als bpr cython cuda linux macos gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="benfred/implicit")