Recommendation System
Showing 12 of 12 projects
A distributed, fast open-source graph database for large-scale data with horizontal scalability and high availability.
A Python library implementing hybrid recommendation algorithms with metadata support for both implicit and explicit feedback.
A unified, comprehensive, and efficient Python/PyTorch library for reproducing and developing recommendation algorithms.
Fast Python library for collaborative filtering recommendation algorithms on implicit feedback datasets.
A comparative Python framework for building, evaluating, and deploying multimodal recommender systems with auxiliary data.
A Python implementation of Restricted Boltzmann Machines for binary factor analysis and collaborative filtering.
A Python implementation of Factorization Machines for recommendation and classification tasks using stochastic gradient descent with adaptive regularization.
A C library for product recommendations using collaborative filtering with fast performance and minimal dependencies.
A collaborative filtering recommendation library in Go that performs random walks on user-item graphs for fast, production-ready recommendations.
Ruby bindings for Annoy (Approximate Nearest Neighbors Oh Yeah), enabling fast nearest neighbor searches in Ruby applications.
An R package implementing Restricted Boltzmann Machines for collaborative filtering and feature learning.
A Python library for training hybrid recommendation systems using scikit-learn algorithms with zero code required.
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