NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems, from feature engineering and preprocessing to training deep learning models and running inference in production.
The main features of nvidia-merlin/merlin are: Recommender Systems.
Open-source alternatives to nvidia-merlin/merlin include: allegro/allrank — allRank is a framework for training learning-to-rank neural models based on PyTorch. benfred/implicit — Implicit is a Python recommendation engine and matrix factorization library designed for collaborative filtering. It… facebookresearch/dlrm — This is a PyTorch recommendation framework and deep learning recommendation model designed to generate personalized… gbolmier/funk-svd. gorse-io/gorse — Gorse is a personalized recommendation engine server and machine learning pipeline designed to suggest items to users… alibaba/easyrec — A framework for large scale recommendation algorithms.
allRank is a framework for training learning-to-rank neural models based on PyTorch.
Implicit is a Python recommendation engine and matrix factorization library designed for collaborative filtering. It implements predictive models that analyze implicit feedback to estimate user preferences and generate personalized item recommendations without requiring explicit ratings. The library utilizes native-code execution and multi-core parallelized processing to decompose large interaction matrices into latent factors. It incorporates approximate nearest neighbor indexing to accelerate high-dimensional similarity lookups and reduce recommendation latency. The framework covers prefer
This is a PyTorch recommendation framework and deep learning recommendation model designed to generate personalized content predictions. It functions as a distributed embedding trainer that processes dense and sparse features through a neural network architecture to predict user preferences. The project implements a CUDA-optimized machine learning system using specialized GPU kernels to accelerate embedding lookup and aggregation. It employs a distributed approach to shard massive sparse feature tables across multiple GPUs, enabling the training of large-scale models. The system utilizes a t
A framework for large scale recommendation algorithms.