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add code to git
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wangzhen38 committed Jun 15, 2022
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2 changes: 1 addition & 1 deletion README_CN.md
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<h2 align="center">最新动态<img src="./doc/imgs/rec_new_icon.png" width="40"/></h2>

* [2022/6/15] 重磅福利:短视频推荐场景下的多任务学习应用实战,欢迎扫码关注:<p align="center"><img width="200" height="200" margin="500" src="./doc/imgs/aistudio_micro_video.png"/></p>
* [2022/6/15] 重磅福利:短视频推荐场景下的多任务学习应用实战视频直播课,欢迎扫码关注:<p align="center"><img width="200" height="200" margin="500" src="./doc/imgs/aistudio_micro_video.png"/></p>
* [2022/6/15] 新增3个前沿算法:[ESCM2](models/multitask/escm2),[MetaHeac](models/multitask/metaheac),[KIM](models/match/kim)
* [2022/5/18] 新增3个前沿算法:[AITM](models/multitask/aitm),[SIGN](models/rank/sign),[DSIN](models/rank/dsin)
* [2022/3/21] 新增[paper](./paper)目录,发布我们对21年的推荐顶会论文的分析,以及工业界的推荐论文列表,供大家参考。
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3 changes: 1 addition & 2 deletions README_EN.md
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<h2 align="center">News<img src="./doc/imgs/rec_new_icon.png" width="40"/></h2>

* [2022/6/15] Excellent course about multi-task learning application under short video recommendation scenarios,welcome to scan the code and follow:
<p align="center"><img width="200" height="200" margin="500" src="./doc/imgs/aistudio_micro_video.png"/></p>
* [2022/6/15] Excellent course about multi-task learning application under short video recommendation scenarios,welcome to scan the code and follow:<p align="center"><img width="200" height="200" margin="500" src="./doc/imgs/aistudio_micro_video.png"/></p>
* [2022/6/15] Add 3 algorithms:[ESCM2](models/multitask/escm2),[MetaHeac](models/multitask/metaheac),[KIM](models/match/kim)
* [2022/5/18] Add 3 algorithms::[AITM](models/multitask/aitm),[SIGN](models/rank/sign),[DSIN](models/rank/dsin)
* [2022/3/21] Add a new [paper](./paper) directory , show our analysis of the top meeting papers of the recommendation system in 2021 years and the list of recommendation system papers in the industry for your reference.
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3 changes: 1 addition & 2 deletions application/multitask_on_video/readme.md
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本项目主要介绍如何使用飞桨大规模推荐算法库[PaddleRec](https://github.com/PaddlePaddle/PaddleRec) ,仅以短视频推荐场景为例,说明多任务技术的应用方法和收益,但多任务技术不仅仅可以使用于视频推荐中,也可以在比如电商场景的点击&转化(购买),信息流推荐场景的点击&点赞(评论,关注等一系列后续行为),搜索广告场景下的点击&再点击(相关广告,相关搜索,猜你喜欢等等)等场景下使用。

另有高级研发工程师直播视频讲解,欢迎扫码关注:
<p align="center"><img width="200" height="200" margin="500" src="./doc/imgs/aistudio_micro_video.png"/></p>
另有高级研发工程师直播视频讲解,欢迎扫码关注:<p align="center"><img width="200" height="200" margin="500" src="./doc/imgs/aistudio_micro_video.png"/></p>
## 2. 什么是多任务学习
### 2.2 多任务学习的概念
多任务学习(multitask)的定义和形式有很多种。我觉得比较直观的理解是和单任务学习相对的一种机器学习方法。
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