cuMF/cumf_als
CUDA Matrix Factorization Library with Alternating Least Square (ALS)
This tool rapidly processes very large datasets of user ratings or interactions to uncover hidden preferences, enabling advanced recommendation systems. It takes in sparse rating matrices, like customer purchases or movie views, and outputs underlying feature matrices that explain these preferences. Data scientists, machine learning engineers, and researchers working with massive user behavior data will find this indispensable.
181 stars. No commits in the last 6 months.
Use this if you need to build collaborative filtering models or perform matrix factorization on extremely large datasets quickly and have access to powerful NVIDIA GPUs.
Not ideal if your datasets are small, you don't have GPU resources, or you require a ready-to-use, off-the-shelf recommendation system without custom development.
Stars
181
Forks
45
Language
Cuda
License
Apache-2.0
Category
Last pushed
Aug 14, 2018
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/cuMF/cumf_als"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
iree-org/iree
A retargetable MLIR-based machine learning compiler and runtime toolkit.
brucefan1983/GPUMD
Graphics Processing Units Molecular Dynamics
uxlfoundation/oneDAL
oneAPI Data Analytics Library (oneDAL)
rapidsai/cuml
cuML - RAPIDS Machine Learning Library
NVIDIA/cutlass
CUDA Templates and Python DSLs for High-Performance Linear Algebra