Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation

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

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

Official code for "DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation" (TPAMI2022) and "Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison" (RecSys2020)

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

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.

Factorization Machine models in PyTorch

LightCTR is a tensorflow 2.0 based, extensible toolbox for building CTR/CVR predicting models.

主流推荐系统Rank算法的实现

Recommender Learning with Tensorflow2.x

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

CTR模型代码和学习笔记总结

一些CTR模型和常见特征工程的方法

A easy library for recommendation system or computational advertising

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