gavince / mtl

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

推荐系统之多目标学习

复现推荐系统中经典的多目标模型，现阶段仓库代码不完善，本人正在不断查漏补缺中

经典模型

SharedBottom

论文地址：An Overview of Multi-Task Learning in Deep Neural Networks

参考代码：DeepCTR

• 模型结构

  

ESMM

论文地址：Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate

参考代码：DeepCTR

• 模型结构

  

MMoE

论文地址：Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts

参考代码：DeepCTR

• 模型结构

  

PLE

论文地址：Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations

参考代码：DeepCTR

• 模型结构

  

相关资料

本人博客

DeepCTR

DeepCTR-Torch

推荐系统中的多任务学习

网易云音乐广告 CTR 预估模型演进过程

Multi-task多任务学习在推荐算法中的应用

多目标排序模型在腾讯 QQ 看点推荐系统中的应用实践

大厂技术干货 | 多目标优化及应用（含代码实现）@推荐与计算广告系列

mtl's Issues

Welcome update to OpenMMLab 2.0

Welcome update to OpenMMLab 2.0

I am Vansin, the technical operator of OpenMMLab. In September of last year, we announced the release of OpenMMLab 2.0 at the World Artificial Intelligence Conference in Shanghai. We invite you to upgrade your algorithm library to OpenMMLab 2.0 using MMEngine, which can be used for both research and commercial purposes. If you have any questions, please feel free to join us on the OpenMMLab Discord at https://discord.gg/amFNsyUBvm or add me on WeChat (van-sin) and I will invite you to the OpenMMLab WeChat group.

Here are the OpenMMLab 2.0 repos branches:

	OpenMMLab 1.0 branch	OpenMMLab 2.0 branch
MMEngine		0.x
MMCV	1.x	2.x
MMDetection	0.x 、1.x、2.x	3.x
MMAction2	0.x	1.x
MMClassification	0.x	1.x
MMSegmentation	0.x	1.x
MMDetection3D	0.x	1.x
MMEditing	0.x	1.x
MMPose	0.x	1.x
MMDeploy	0.x	1.x
MMTracking	0.x	1.x
MMOCR	0.x	1.x
MMRazor	0.x	1.x
MMSelfSup	0.x	1.x
MMRotate	1.x	1.x
MMYOLO		0.x

Attention: please create a new virtual environment for OpenMMLab 2.0.