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‎README_CN.md‎

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| 排序 | [AutoFIS](models/rank/autofis/) | - | ✓ | ✓ | >=2.1.0 | [KDD 2020][AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction](https://arxiv.org/pdf/2003.11235v3.pdf) |
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| 排序 | [DCN_V2](models/rank/dcn_v2/) | - | ✓ | ✓ | >=2.1.0 | [WWW 2021][DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://arxiv.org/pdf/2008.13535v2.pdf)|
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| 排序 | [AITM](models/rank/aitm/) | - | ✓ | ✓ | >=2.1.0 | [KDD 2021][Modeling the Sequential Dependence among Audience Multi-step Conversions withMulti-task Learning in Targeted Display Advertising](https://arxiv.org/pdf/2105.08489v2.pdf) |
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| 排序 | [DSIN](models/rank/dsin/) | - | ✓ | ✓ | >=2.1.0 | [IJCAI 2019][Deep Session Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.06482v1.pdf) |
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| 排序 | [SIGN](models/rank/sign/)([文档](https://paddl7erec.readthedocs.io/en/latest/models/rank/sign.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3869111) | ✓ | ✓ | >=2.1.0 | [AAAI 2021][Detecting Beneficial Feature Interactions for Recommender Systems](https://arxiv.org/pdf/2008.00404v6.pdf) |
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| 多任务 | [PLE](models/multitask/ple/)([文档](https://paddlerec.readthedocs.io/en/latest/models/multitask/ple.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238938) | ✓ | ✓ | >=2.1.0 | [RecSys 2020][Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://dl.acm.org/doi/abs/10.1145/3383313.3412236) |
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| 多任务 | [ESMM](models/multitask/esmm/)([文档](https://paddlerec.readthedocs.io/en/latest/models/multitask/esmm.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238583) | ✓ | ✓ | >=2.1.0 | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931) |

‎README_EN.md‎

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| Rank | [AutoFIS](models/rank/autofis/) | - | ✓ | ✓ | >=2.1.0 | [KDD 2020][AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction](https://arxiv.org/pdf/2003.11235v3.pdf) |
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| Rank | [DCN_V2](models/rank/dcn_v2/) | - | ✓ | ✓ | >=2.1.0 | [WWW 2021][DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://arxiv.org/pdf/2008.13535v2.pdf)|
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| Rank | [AITM](models/rank/aitm/) | - | ✓ | ✓ | >=2.1.0 | [KDD 2021][Modeling the Sequential Dependence among Audience Multi-step Conversions withMulti-task Learning in Targeted Display Advertising](https://arxiv.org/pdf/2105.08489v2.pdf) |
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| Rank | [DSIN](models/rank/dsin/) | - | ✓ | ✓ | >=2.1.0 | [IJCAI 2019][Deep Session Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.06482v1.pdf) |
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| Rank | [SIGN](models/rank/sign/)([doc](https://paddlerec.readthedocs.io/en/latest/models/rank/sign.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3869111) | ✓ | ✓ | >=2.1.0 | [AAAI 2021][Detecting Beneficial Feature Interactions for Recommender Systems](https://arxiv.org/pdf/2008.00404v6.pdf) |
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| Multi-Task | [PLE](models/multitask/ple/)<br>([doc](https://paddlerec.readthedocs.io/en/latest/models/multitask/ple.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238938) | ✓ | ✓ | >=2.1.0 | [RecSys 2020][Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://dl.acm.org/doi/abs/10.1145/3383313.3412236) |
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| Multi-Task | [ESMM](models/multitask/esmm/)<br>([doc](https://paddlerec.readthedocs.io/en/latest/models/multitask/esmm.html)) | [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238583) | ✓ | ✓ | >=2.1.0 | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931) |

‎datasets/Ali_Display_Ad_Click_DSIN/get_data.sh‎

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mkdir raw_data
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cd raw_data
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wget https://paddlerec.bj.bcebos.com/datasets/dmr/user_profile.csv.tar.gz
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tar -zxvf user_profile.csv.tar.gz
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wget https://paddlerec.bj.bcebos.com/datasets/dmr/raw_sample.csv.tar.gz
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tar -zxvf raw_sample.csv.tar.gz
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wget https://paddlerec.bj.bcebos.com/datasets/dmr/behavior_log.csv.tar.gz
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tar -zxvf behavior_log.csv.tar.gz
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wget https://paddlerec.bj.bcebos.com/datasets/dmr/ad_feature.csv.tar.gz
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tar -zxvf ad_feature.csv.tar.gz

‎datasets/Ali_Display_Ad_Click_DSIN/readme.md‎

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# Ali_Display_Ad_Click数据集
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[Ali_Display_Ad_Click](https://tianchi.aliyun.com/dataset/dataDetail?dataId=56)是阿里巴巴提供的一个淘宝展示广告点击率预估数据集
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## 原始数据集介绍
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- 原始样本骨架raw_sample：淘宝网站中随机抽样了114万用户8天内的广告展示/点击日志（2600万条记录），构成原始的样本骨架
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1. user：脱敏过的用户ID；
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2. adgroup_id：脱敏过的广告单元ID；
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3. time_stamp：时间戳；
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4. pid：资源位；
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5. nonclk：为1代表没有点击；为0代表点击；
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6. clk：为0代表没有点击；为1代表点击；
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```
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user,time_stamp,adgroup_id,pid,nonclk,clk
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581738,1494137644,1,430548_1007,1,0
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```
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- 广告基本信息表ad_feature：本数据集涵盖了raw_sample中全部广告的基本信息
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1. adgroup_id：脱敏过的广告ID；
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2. cate_id：脱敏过的商品类目ID；
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3. campaign_id：脱敏过的广告计划ID；
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4. customer: 脱敏过的广告主ID；
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5. brand：脱敏过的品牌ID；
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6. price: 宝贝的价格
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```
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adgroup_id,cate_id,campaign_id,customer,brand,price
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63133,6406,83237,1,95471,170.0
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```
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- 用户基本信息表user_profile：本数据集涵盖了raw_sample中全部用户的基本信息
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1. userid：脱敏过的用户ID；
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2. cms_segid：微群ID；
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3. cms_group_id：cms_group_id；
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4. final_gender_code：性别 1:男,2:女；
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5. age_level：年龄层次； 1234
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6. pvalue_level：消费档次，1:低档，2:中档，3:高档；
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7. shopping_level：购物深度，1:浅层用户,2:中度用户,3:深度用户
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8. occupation：是否大学生 ，1:是,0:否
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9. new_user_class_level：城市层级
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```
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userid,cms_segid,cms_group_id,final_gender_code,age_level,pvalue_level,shopping_level,occupation,new_user_class_level
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234,0,5,2,5,,3,0,3
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```
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- 用户的行为日志behavior_log：本数据集涵盖了raw_sample中全部用户22天内的购物行为
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1. user：脱敏过的用户ID；
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2. time_stamp：时间戳；
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3. btag：行为类型, 包括以下四种：(pv:浏览),(cart:加入购物车),(fav:喜欢),(buy:购买)
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4. cate：脱敏过的商品类目id；
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5. brand: 脱敏过的品牌id；
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```
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user,time_stamp,btag,cate,brand
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558157,1493741625,pv,6250,91286
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```
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## 预处理数据集介绍
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对原始数据集中的四个文件，参考[原论文的数据预处理过程](https://github.com/shenweichen/DSIN/tree/master/code)对数据进行处理，形成满足DSIN论文条件且可以被reader直接读取的数据集。
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数据集共有八个pkl文件，训练集和测试集各自拥有四个，以训练集为例，这四个文件为train_feat_input.pkl、train_sess_input、train_sess_length和train_label.pkl。各自存储了按0.25的采样比进行采样后的user及item特征输入，用户会话特征输入、用户会话长度和标签数据。

‎datasets/Ali_Display_Ad_Click_DSIN/run.sh‎

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mkdir big_train
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mkdir big_test
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wget -O model_input.tar.gz https://bj.bcebos.com/v1/ai-studio-online/53e61a9bcfc54e0581044883d0f876d9841cb4d0a68848f1a1d568a84591da6f?responseContentDisposition=attachment%3B%20filename%3Dmodel_input.tar.gz&authorization=bce-auth-v1%2F0ef6765c1e494918bc0d4c3ca3e5c6d1%2F2022-04-21T01%3A43%3A00Z%2F-1%2F%2F665a728726f0569e1ef9dd423adfa40a2a5e798f86a8d5d68804a2f21cc03624
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tar -zxvf model_input.tar.gz
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mv model_input/test_feat_input.pkl big_test/
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mv model_input/test_label.pkl big_test/
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mv model_input/test_sess_input.pkl big_test/
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mv model_input/test_session_length.pkl big_test/
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mv model_input/train_feat_input.pkl big_train/
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mv model_input/train_label.pkl big_train/
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mv model_input/train_sess_input.pkl big_train/
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mv model_input/train_session_length.pkl big_train/

‎doc/imgs/dsin.png‎

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‎doc/source/models/rank/dsin.md‎

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# dsin (Deep Session Interest Network for Click-Through Rate Prediction)
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代码请参考：[dsin](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/dsin)
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如果我们的代码对您有用，还请点个star啊~
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## 内容
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- [模型简介](#模型简介)
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- [数据准备](#数据准备)
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- [运行环境](#运行环境)
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- [快速开始](#快速开始)
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- [模型组网](#模型组网)
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- [效果复现](#效果复现)
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- [进阶使用](#进阶使用)
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- [FAQ](#FAQ)
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## 模型简介
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本模型主要聚焦于用户的历史会话行为，通过Self-Attention和BiLSTM对历史会话行为进行学习，最后通过Activation Unit得到最终的session表征向量，再结合其他特征送入MLP计算最后的ctr score。[Deep Session Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.06482v1.pdf)文章通过 Transformer 和 BiLSTM 来学习用户的 Session Interest Interacting，提升模型的表达能力。
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## 数据准备
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本模型使用论文中的数据集Alimama Dataset，参考[原文作者的数据预处理过程](https://github.com/shenweichen/DSIN/tree/master/code)对数据进行处理。在模型目录的data目录下为您准备了快速运行的示例数据，若需要使用全量数据可以参考下方[效果复现](#效果复现)部分。
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## 运行环境
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PaddlePaddle>=2.0
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python 3.5/3.6/3.7
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os : windows/linux/macos
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## 快速开始
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本文提供了样例数据可以供您快速体验，在任意目录下均可执行。在DSIN模型目录的快速执行命令如下：
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```bash
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# 进入模型目录
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# cd models/rank/dmr # 在任意目录均可运行
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# 动态图训练
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python -u ../../../tools/trainer.py -m config.yaml # 全量数据运行config_bigdata.yaml
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# 动态图预测
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python -u ../../../tools/infer.py -m config.yaml
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# 静态图训练
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python -u ../../../tools/static_trainer.py -m config.yaml # 全量数据运行config_bigdata.yaml
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# 静态图预测
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python -u ../../../tools/static_infer.py -m config.yaml
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```
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## 模型组网
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论文[Deep Session Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.06482v1.pdf)中的网络结构如图所示:
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<p align="center">
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<img align="center" src="../../../doc/imgs/dsin.png">
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<p>
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## 效果复现
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为了方便使用者能够快速的跑通每一个模型，我们在每个模型下都提供了样例数据。如果需要复现readme中的效果,请按如下步骤依次操作即可。
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在全量数据下模型的指标如下：
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| 模型 | auc | batch_size | epoch_num | Time of each epoch |
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| :------| :------ | :------ | :------| :------ |
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| DSIN | 0.6356 | 4096 | 1 | 约10分钟 |
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1. 确认您当前所在目录为PaddleRec/models/rank/dsin
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2. 进入paddlerec/datasets/Ali_Display_Ad_Click_DSIN目录下，执行该脚本，会从国内源的服务器上下载我们预处理完成的Alimama全量数据集，并解压到指定文件夹。若您希望从原始数据集自行处理，请详见该目录下的readme。
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``` bash
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cd ../../../datasets/Ali_Display_Ad_Click_DSIN
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sh run.sh
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```
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3. 切回模型目录,执行命令运行全量数据
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```bash
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cd - # 切回模型目录
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# 动态图训练
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python -u ../../../tools/trainer.py -m config_bigdata.yaml # 全量数据运行config_bigdata.yaml
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python -u ../../../tools/infer.py -m config_bigdata.yaml # 全量数据运行config_bigdata.yaml
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```
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效果复现过程可参考[AI Studio项目](https://aistudio.baidu.com/aistudio/projectdetail/3850087)。
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Note:运行环境为至尊GPU。
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## 进阶使用
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## FAQ

‎doc/source/readme.md‎

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[deeprec](https://paddlerec.readthedocs.io/en/latest/models/rank/deeprec.html)
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[autofis](https://paddlerec.readthedocs.io/en/latest/models/rank/autofis.html)
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[aitm](https://paddlerec.readthedocs.io/en/latest/models/rank/aitm.html)
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[dsin](https://paddlerec.readthedocs.io/en/latest/models/rank/dsin.html)
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[sign](https://paddlerec.readthedocs.io/en/latest/models/rank/sign.html)

‎models/rank/dnn/config.yaml‎

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infer_load_path: "output_model_dnn"
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infer_start_epoch: 0
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infer_end_epoch: 3
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num_workers: 0
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# distribute_config
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sync_mode: "async"

‎models/rank/dnn/config_bigdata.yaml‎

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infer_load_path: "output_model_dnn_all"
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infer_end_epoch: 4
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num_workers: 0
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#thread_num: 5
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#reader_type: "QueueDataset" # DataLoader / QueueDataset / RecDataset

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