Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

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.

Recommender Learning with Tensorflow2.x

Factorization Machine models in PyTorch

原理解析及代码实战，推荐算法也可以很简单 🔥 想要系统的学习推荐算法的小伙伴，欢迎 Star 或者 Fork 到自己仓库进行学习🚀 有任何疑问欢迎提 Issues，也可加文末的联系方式向我询问！

CTR模型代码和学习笔记总结

Implements of Awesome RecSystem Models with PyTorch/TF2.0

主流推荐系统Rank算法的实现

FFM (Field-Awared Factorization Machine) on Spark

基于 Pytorch 实现推荐系统相关的算法

🔥 全面深入地掌握NDK技术，成为下一波5G时代的浪潮儿~

Python Wrapped LibFFM

Tensorflow2.x implementations of CTR（LR、FM、FFM)

Field-aware factorization machine (FFM) with FTRL

some ctr model, implemented by PyTorch, such as Factorization Machines, Field-aware Factorization Machines, DeepFM, xDeepFM, Deep Interest Network

A easy library for recommendation system or computational advertising

Call Java from C++ and C++ from Java with a variety of old and new projects like JNI, JNA, JNR, FFM, JExtract, GraalVM, JNI-Bind, etc.

Pure Python implementation of Telea FMM inpainting

Unsupervised learning coupled with applied factor analysis to the five-factor model (FFM), a taxonomy for personality traits used to describe the human personality and psyche, via descriptors of common language and not on neuropsychological experiments. Used kmeans clustering and feature scaling (min-max normalization).

rater, recommender systems. 推荐模型，包括：DeepFM，Wide&Deep，DIN，DeepWalk，Node2Vec等模型实现，开箱即用。