nfm 开源项目推荐（26 个）

shenweichen /DeepCTR

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

ZiyaoGeng /RecLearn

Recommender Learning with Tensorflow2.x

rixwew /pytorch-fm

Factorization Machine models in PyTorch

huangjunheng /recommendation_model

练习下用pytorch来复现下经典的推荐系统模型，如MF， FM， DeepConn， MMOE， PLE， DeepFM， NFM， DCN， AFM， AutoInt， ONN， FiBiNET， DCN-v2， AFN， DCAP等

yudi-mars /daisyRec

“DaisyRec 2.0：严谨评估的基准测试建议”（TPAMI2022）和“我们是否在进行严格评估？”的官方代码可重复性评估与公平比较的基准测试建议（RecSys2020）

DSXiangLi /CTR

CTR模型代码和学习笔记总结

tangxyw /RecAlgorithm

主流推荐系统Rank算法的实现

sisinflab /elliot

Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

QikaiXu /Recommender-System-Pytorch

基于 Pytorch 实现推荐系统相关的算法

Hirosora /LightCTR

LightCTR 是一个基于张量流 2.0 的可扩展工具箱，用于构建 CTR/CVR 预测模型。

shawroad /DeepCTR-pytorch

Here are the models listed in CTR. Example: FM、DeepFM、xDeepFM etc.

guoyang9 /NFM-pyorch

A pytorch implementation for He et al. Neural Factorization Machines for Sparse Predictive Analytics on SIGIR 2017.

HanpyBin /recommendation-system-model

Implementation with Pytorch of DeepCrossing, DeepFM,NFM,Wide&Deep

TIXhjq /ML_Function

一些CTR模型和常见特征工程的方法

End-the-cold-night /deeprec

A easy library for recommendation system or computational advertising

simonholliday /substation

High performance SDR band scanner - detects, demodulates, and records radio transmissions automatically with CTCSS/DCS detection, three-layer noise rejection, and broadcast-standard metadata.

k3rielit /nfm3

Need for Madness 3

Top-Models /TOMMY

TOMMY is een topic modelling applicatie die ontwikkeld is door studenten van de Universiteit Utrecht in opdracht van EMMA. Voor het uitvoeren van topic modelling wordt het achterliggende Latent Dirichlet Allocation (LDA) algoritme of Non-Negative Matrix Factorization (NMF) algoritme uitgevoerd op de door de gebruiker aangeleverde bestanden.

DragShot /ds-addons-files

Patch repository for NfmM DS-addons

jisilvia /Wine_Quality_Analysis

Several wines are analyzed in order to gain insights on what features affect a wine's rating using logistic regressions models, kNN algorithms, topic modeling, LDA, and Gensim models.