Recommendation Systems
Showing 10 of 10 projects
A deep learning library built on PyTorch that provides high-level components for rapid results and low-level components for research flexibility.
A deep learning library built on PyTorch that provides high-level components for rapid results and low-level components for research flexibility.
A repository of examples, utilities, and best practices for building and deploying production-ready recommendation systems.
An open-source library for training and deploying deep learning recommendation models with sparse data at scale using multi-GPU support.
A high-performance, easy-to-use, and scalable machine learning package for linear models, factorization machines, and field-aware factorization machines.
A minimalist neural network library optimized for sparse data and single-machine environments.
A scalable machine learning library for training Generalized Linear Models and GLMix models on Apache Spark.
Cleora is a fast, deterministic graph embedding engine that computes all random walks in a single matrix multiplication, requiring no GPUs or negative sampling.
A Go library implementing collaborative filtering algorithms for recommendation systems, including ALS, Bayesian, and similarity-based approaches.
A Julia package providing multiple algorithms for non-negative matrix factorization, including multiplicative updates, ALS, coordinate descent, and separable NMF.
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