mmoe

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

Recommendation Algorithm大规模推荐算法库，包含推荐系统经典及最新算法LR、Wide&Deep、DSSM、TDM、MIND、Word2Vec、Bert4Rec、DeepWalk、SSR、AITM，DSIN，SIGN，IPREC、GRU4Rec、Youtube_dnn、NCF、GNN、FM、FFM、DeepFM、DCN、DIN、DIEN、DLRM、MMOE、PLE、ESMM、ESCMM, MAML、xDeepFM、DeepFEFM、NFM、AFM、RALM、DMR、GateNet、NAML、DIFM、Deep Crossing、PNN、BST、AutoInt、FGCNN、FLEN、Fibinet、ListWise、DeepRec、ENSFM，TiSAS，AutoFI…

A PyTorch Library for Multi-Task Learning

MLGB is a library that includes many models of CTR Prediction & Recommender System by TensorFlow & PyTorch. 「妙计包」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。

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

主流推荐系统Rank算法的实现

multi task mode for esmm and mmoe

用pytorch 方法复现了二十多个经典的推荐算法论文，其中包含排序论文和推荐召回论文，并在demo里面选了一个召回模型和排序模型的运行示例。

学习并复现经典的推荐系统多目标任务，如：SharedBottom、ESMM、MMoE、PLE

基于ESMM、MMoE和deepFM的多目标模型

A simple package about multi-task recommendation

A framework for Ranked List Truncation, including the implementation of multiple existing deep models, such as BiCut、Choopy and AttnCut. And we propose the multi-task model based on these models.

A comprehensive toolkit package designed to help you accurately predict key metrics in commercial area

Feature engineering Wechat video channel for multi-task model

End-to-end multi-task recommendation system with SASRec, MMoE, real KuaiRand evaluation, FastAPI serving, and an interactive model lab.

A sequential recommendation system that predicts what users will watch next and how they'll rate it using SASRec and multi-task learning.

Leakage-safe two-stage video recommendation with point-in-time features, randomized exposure evaluation, and production-style serving.

Two-stage retrieval→ ranking recommender system. Two-Tower + MMoE-DeepFM, zero train/serve skew, ONNX serving. Runnable end-to-end.

Review on Google's multitask ranking system by comparing to other methods used in recommender systems

Multimodal multi-task short-video recommendation with PLE, MMoE, uncertainty weighting, PCGrad, gate analysis, and modality ablation.