HugeCTR is a high efficiency GPU framework designed for Click-Through-Rate (CTR) estimating training
-
Updated
May 21, 2024 - C++
HugeCTR is a high efficiency GPU framework designed for Click-Through-Rate (CTR) estimating training
Fast Open-Source Search & Clustering engine × for Vectors & 🔜 Strings × in C++, C, Python, JavaScript, Rust, Java, Objective-C, Swift, C#, GoLang, and Wolfram 🔍
A high-performance framework for training wide-and-deep recommender systems on heterogeneous cluster
Recommender System Using Parallel Matrix Factorization
User Based Movie Recommendation System based on Collaborative Filtering Using Netflix Movie Dataset
Search/Recommendation engine and metainformation server for fanfiction net
Qt C++ 图书推荐与评论系统GUI 协同过滤推荐 collaborative filtering, book recommendation System, Book-Crossing Dataset
矩阵分解(BPRMF) + 知识图谱表示学习(TransR) 构建的推荐系统
Code for "Content-Based Social Recommendation with Poisson Matrix Factorization" (ECML-PKDD 2017)
Dictionary designed for read-mostly scene.
Movie recommendation system implemented in C++ and trained/evaluated on Netflix Prize dataset.
FEATure HashER
CS399(Machine Learning) final research project by Team Luke
A morden implement of CHD/SHD algorithm.
A node addon for recommendations
Fast Factorization Machines
(ReCA) Recommendation with Context Awareness using Multi-Environment Markov Decision Processes
Parallelisation of a matrix factorisation recommender system using OpenMP and MPI.
An item-based collaborative filtering recommender system built from scratch in C++
A movie recommendation system, or a movie recommender system, is an ML-based approach to filtering or predicting the users' film preferences based on their past choices and behavior.
Add a description, image, and links to the recommender-system topic page so that developers can more easily learn about it.
To associate your repository with the recommender-system topic, visit your repo's landing page and select "manage topics."