Merge branch 'master' of https://github.com/PaddlePaddle/PaddleRec

Author: esythan

README_CN.md

@@ -8,6 +8,7 @@
 
 <h2 align="center">News<img src="./doc/imgs/rec_new_icon.png" width="40"/></h2>
 
+* [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.  
 * [2022/3/10] Add 5 algorithms: [DCN_V2](models/rank/dcn_v2), [MHCN](models/recall/mhcn), [FLEN](models/rank/flen), [Dselect_K](models/multitask/dselect_k)，[AutoFIS](models/rank/autofis)。  
 * [2022/1/12] Add AI Studio [Online running](https://aistudio.baidu.com/aistudio/projectdetail/3240640) function, you can easily and quickly online experience our model on AI studio platform.
@@ -159,13 +160,18 @@ python -u tools/static_trainer.py -m models/rank/dnn/config.yaml #  Training wit
   |         Rank          |                     [FLEN](models/rank/flen/)                     |  -  |         ✓         |     ✓     |  >=2.1.0 | [2019][FLEN: Leveraging Field for Scalable CTR Prediction]( https://arxiv.org/pdf/1911.04690.pdf)                                                                                                           |
   |   Rank   |                     [DeepRec](models/rank/deeprec/)                     |  -  |       ✓     |     ✓     | >=2.1.0 | [2017][Training Deep AutoEncoders for Collaborative Filtering](https://arxiv.org/pdf/1708.01715v3.pdf)                                                                                                          |
   |   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)                                                                                                          |
-  |   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) 
+  |   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)|
+  |   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)  |
+  |   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) |
+  |   Rank   |                                      [IPRec](models/rank/iprec/)([doc](https://paddl7erec.readthedocs.io/en/latest/models/rank/iprec.html))                                       |                                      -                                      |       ✓     |     ✓     | >=2.1.0 | [SIGIR 2021][Package Recommendation with Intra- and Inter-Package Attention Networks](http://nlp.csai.tsinghua.edu.cn/~xrb/publications/SIGIR-21_IPRec.pdf)                             |
+  |   Multi-Task   |                                                                          [AITM](models/rank/aitm/)                                                                          |  -  |       ✓     |     ✓     | >=2.1.0 | [KDD 2021][Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](https://arxiv.org/pdf/2105.08489v2.pdf)  |
   |      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)                                                              |
   |      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)                                                              |
   |      Multi-Task       |                  [MMOE](models/multitask/mmoe/)<br>([doc](https://paddlerec.readthedocs.io/en/latest/models/multitask/mmoe.html))                   |  [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238934)  |         ✓         |     ✓     |      >=2.1.0     | [KDD 2018][Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007)                                                       |
   |      Multi-Task       |           [ShareBottom](models/multitask/share_bottom/)<br>([doc](https://paddlerec.readthedocs.io/en/latest/models/multitask/share_bottom.html))           |  [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238943)  |         ✓         |     ✓     |  >=2.1.0 | [1998][Multitask learning](http://reports-archive.adm.cs.cmu.edu/anon/1997/CMU-CS-97-203.pdf)                                                                                                               |
   |      Multi-Task       |           [Maml](models/multitask/maml/)<br>([doc](https://paddlerec.readthedocs.io/en/latest/models/multitask/maml.html))           |  [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238412)  |    x      |     x     | >=2.1.0 | [PMLR 2017][Model-agnostic meta-learning for fast adaptation of deep networks](https://arxiv.org/pdf/1703.03400.pdf)                                                                                                               |
   |  Multi-Task  |           [DSelect_K](models/multitask/dselect_k/)<br>([doc](https://paddlerec.readthedocs.io/en/latest/models/multitask/dselect_k.html))           |  -  |      x      |     x     | >=2.1.0 | [NeurIPS 2021][DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning](https://arxiv.org/pdf/2106.03760v3.pdf)                                                                                                               |
+  |  Multi-Task  |                         [ESCM2](models/multitask/escm2/)                          |  -  |      x      |     x     | >=2.1.0 | [SIGIR 2022][ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation](https://arxiv.org/pdf/2204.05125.pdf)                                                                                                               |
   |        Re-Rank        |                [Listwise](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rerank/listwise/)                |  -  |         ✓         |     x     |  [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [2019][Sequential Evaluation and Generation Framework for Combinatorial Recommender System](https://arxiv.org/pdf/1902.00245.pdf)                                                                           |
 
 <h2 align="center">Community</h2>

README_EN.md

@@ -20,5 +20,6 @@
   |                     [FLEN](models/rank/flen/)                     |  [LinJayan](https://github.com/LinJayan)  |    https://github.com/PaddlePaddle/PaddleRec/pull/685   | 论文复现赛第五期 |
   |                     [MHCN](models/recall/mhcn/)                     |  [Andy1314Chen](https://github.com/Andy1314Chen)  |    https://github.com/PaddlePaddle/PaddleRec/pull/679   | 论文复现赛第五期 |
   |                     [DCN_V2](models/rank/dcn_v2/)                     |  [LinJayan](https://github.com/LinJayan)  |    https://github.com/PaddlePaddle/PaddleRec/pull/677   | 论文复现赛第五期 |
+  |                     [SIGN](models/rank/sign/)                     |  [BamLubi](https://github.com/BamLubi)  |    https://github.com/PaddlePaddle/PaddleRec/pull/748   | 论文复现赛第六期 |
 
 </div> 

contributor.md

@@ -0,0 +1,10 @@
+mkdir raw_data
+cd raw_data
+wget https://paddlerec.bj.bcebos.com/datasets/dmr/user_profile.csv.tar.gz
+tar -zxvf user_profile.csv.tar.gz
+wget https://paddlerec.bj.bcebos.com/datasets/dmr/raw_sample.csv.tar.gz
+tar -zxvf raw_sample.csv.tar.gz
+wget https://paddlerec.bj.bcebos.com/datasets/dmr/behavior_log.csv.tar.gz
+tar -zxvf behavior_log.csv.tar.gz
+wget https://paddlerec.bj.bcebos.com/datasets/dmr/ad_feature.csv.tar.gz
+tar -zxvf ad_feature.csv.tar.gz

datasets/Ali_Display_Ad_Click_DSIN/get_data.sh

@@ -0,0 +1,58 @@
+# Ali_Display_Ad_Click数据集
+[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
+
+## 原始数据集介绍
+- 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
+1. user：脱敏过的用户ID；
+2. adgroup_id：脱敏过的广告单元ID；
+3. time_stamp：时间戳；
+4. pid：资源位；
+5. nonclk：为1代表没有点击；为0代表点击；
+6. clk：为0代表没有点击；为1代表点击；
+
+```
+user,time_stamp,adgroup_id,pid,nonclk,clk
+581738,1494137644,1,430548_1007,1,0
+```
+
+- 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
+1. adgroup_id：脱敏过的广告ID；
+2. cate_id：脱敏过的商品类目ID；
+3. campaign_id：脱敏过的广告计划ID；
+4. customer: 脱敏过的广告主ID；
+5. brand：脱敏过的品牌ID；
+6. price: 宝贝的价格
+```
+adgroup_id,cate_id,campaign_id,customer,brand,price
+63133,6406,83237,1,95471,170.0
+```
+
+- 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
+1. userid：脱敏过的用户ID；
+2. cms_segid：微群ID；
+3. cms_group_id：cms_group_id；
+4. final_gender_code：性别 1:男,2:女；
+5. age_level：年龄层次； 1234
+6. pvalue_level：消费档次，1:低档，2:中档，3:高档；
+7. shopping_level：购物深度，1:浅层用户,2:中度用户,3:深度用户
+8. occupation：是否大学生 ，1:是,0:否
+9. new_user_class_level：城市层级
+```
+userid,cms_segid,cms_group_id,final_gender_code,age_level,pvalue_level,shopping_level,occupation,new_user_class_level 
+234,0,5,2,5,,3,0,3
+```
+
+- 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
+1. user：脱敏过的用户ID；
+2. time_stamp：时间戳；
+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)
+4. cate：脱敏过的商品类目id；
+5. brand: 脱敏过的品牌id；
+```
+user,time_stamp,btag,cate,brand
+558157,1493741625,pv,6250,91286
+```
+
+## 预处理数据集介绍
+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。
+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/readme.md

@@ -0,0 +1,12 @@
+mkdir big_train
+mkdir big_test
+wget https://paddlerec.bj.bcebos.com/datasets/Ali_Display_Ad_Click/model_input.tar.gz
+tar -zxvf model_input.tar.gz
+mv model_input/test_feat_input.pkl big_test/
+mv model_input/test_label.pkl big_test/
+mv model_input/test_sess_input.pkl big_test/
+mv model_input/test_session_length.pkl big_test/
+mv model_input/train_feat_input.pkl big_train/
+mv model_input/train_label.pkl big_train/
+mv model_input/train_sess_input.pkl big_train/
+mv model_input/train_session_length.pkl big_train/

datasets/Ali_Display_Ad_Click_DSIN/run.sh

@@ -14,7 +14,7 @@
 
 
 runner:
-  raw_file_dir: "path"  # raw_data dir
+  raw_file_dir: "raw_file/train"  # raw_data dir
   raw_filled_file_dir: "./raw_data" # raw_data_filled dir
   train_data_dir: "./train_data_full"  # train datasets
   test_data_dir:  "./test_data_full"  # test datasets

datasets/Avazu_flen/data_config.yaml

@@ -59,7 +59,7 @@ def __init__(self, config):
         self.min_threshold = self.config.get("runner.min_threshold")
         self.feature_map_cache = self.config.get("runner.feature_map_cache")
 
-        # self.filled_raw()
+        self.filled_raw()
 
         self.init()
 

datasets/Avazu_flen/preprocess.py

@@ -2,11 +2,11 @@
 #### 1.Get raw datasets:
 you can go to：[https://www.kaggle.com/c/avazu-ctr-prediction/data](https://www.kaggle.com/c/avazu-ctr-prediction)
 
-将下载的原始数据目录配置在data_config.yaml中，执行命令获取全量数据
+将下载的数据解压后，只保留训练集即可，且命名为`train``
 
 | 名称 | 说明 | 
 | -------- | -------- | 
-| raw_file_dir | 原始数据集目录 |
+| raw_file | 原始数据集目录 |
 | raw_filled_file_dir     | 原始数据缺失值处理后的目录     |
 |   train_data_dir   | 训练集存放目录     | 
 |   test_data_dir   | 测试集存放目录     | 
@@ -15,9 +15,9 @@ you can go to：[https://www.kaggle.com/c/avazu-ctr-prediction/data](https://www
 | feature_map_cache     | 特征缓存数据     | 
 
 
-
+然后执行脚本
 ```bash
-sh data_process.sh
+sh run.sh
 ```
 #### 2.Get preprocessd datasets:
 you can also go to: [AiStudio数据集](https://aistudio.baidu.com/aistudio/datasetdetail/125200)

datasets/Avazu_flen/readme.md

@@ -1 +1,7 @@
+mkdir train_data_full
+mkdir test_data_full
+mkdir raw_file
+mkdir raw_filled_file_dir
+mv train ./raw_file
+
 python preprocess.py -m data_config.yaml

datasets/Avazu_flen/run.sh

@@ -0,0 +1,7 @@
+mkdir data
+mkdir data/whole_data && mkdir data/whole_data/train && mkdir data/whole_data/test
+tar zxvf data/sample_train.tar.gz -C data
+tar zxvf data/sample_test.tar.gz -C data
+python process_public_data.py
+mv data/ctr_cvr.train data/whole_data/train
+mv data/ctr_cvr.test data/whole_data/test