A high-performance toolkit for LR/FM training on large-scale sparse data.
-
Updated
Apr 3, 2024 - C++
A high-performance toolkit for LR/FM training on large-scale sparse data.
Running field-aware factorization machines on the Criteo data
Fast Factorization Machines
A Python/C++ implementation of Bayesian Factorization Machines
Lightweight and Scalable framework that combines mainstream algorithms of Click-Through-Rate prediction based computational DAG, philosophy of Parameter Server and Ring-AllReduce collective communication.
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
Add a description, image, and links to the factorization-machines topic page so that developers can more easily learn about it.
To associate your repository with the factorization-machines topic, visit your repo's landing page and select "manage topics."