Reusable deep learning models for recommendation systems
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Updated
Aug 20, 2020 - Python
Reusable deep learning models for recommendation systems
AlitaNet: A click through rate (ctr) prediction deep learning Network implementation with TensorFlow, including LR, FM, AFM, Wide&Deep, DeepFM, xDeepFM, AutoInt, FiBiNet, LS-PLM, DCN, etc.
Explicit high order interaction models implemented in Keras, including: DCN, xDeepFM, AutoInt etc.
rater, recommender systems. 推荐模型,包括:DeepFM,Wide&Deep,DIN,DeepWalk,Node2Vec等模型实现,开箱即用。
A easy library for recommendation system or computational advertising
some ctr model, implemented by PyTorch, such as Factorization Machines, Field-aware Factorization Machines, DeepFM, xDeepFM, Deep Interest Network
MLGB is a library that includes many models of CTR Prediction & Recommender System by TensorFlow & PyTorch. MLGB是一个包含50+点击率预估和推荐系统深度模型的、通过TensorFlow和PyTorch撰写的库。
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
CTR模型代码和学习笔记总结
DeepTables: Deep-learning Toolkit for Tabular data
Factorization Machine models in PyTorch
CTR prediction models based on deep learning(基于深度学习的广告推荐CTR预估模型)
Recommender Learning with Tensorflow2.x
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