ffm

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

🔥 全面深入地掌握NDK技术，成为下一波5G时代的浪潮儿~

基于 Pytorch 实现推荐系统相关的算法

FFM (Field-Awared Factorization Machine) on Spark

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

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

Python Wrapped LibFFM

Complete libvips bindings for Java, Kotlin, and JVM projects using the Foreign Function and Memory API (FFM)

Pure Python implementation of Telea FMM inpainting

Field-aware factorization machine (FFM) with FTRL

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

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

A easy library for recommendation system or computational advertising