update docs (#317)

update docs

.github/ISSUE_TEMPLATE/bug_report.md

@@ -19,8 +19,8 @@ Steps to reproduce the behavior:
 
 **Operating environment(运行环境):**
  - python version [e.g. 3.5, 3.7]
- - tensorflow version [e.g. 1.4.0, 1.15.0, 2.3.0]
- - deepctr version [e.g. 0.8.2,]
+ - tensorflow version [e.g. 1.4.0, 1.15.0, 2.4.0]
+ - deepctr version [e.g. 0.8.3,]
 
 **Additional context**
 Add any other context about the problem here.

.github/ISSUE_TEMPLATE/question.md

@@ -16,5 +16,5 @@ Add any other context about the problem here.
 
 **Operating environment(运行环境):**
  - python version [e.g. 3.6]
- - tensorflow version [e.g. 1.4.0, 1.5.0, 2.3.0]
- - deepctr version [e.g. 0.8.2,]
+ - tensorflow version [e.g. 1.4.0, 1.5.0, 2.4.0]
+ - deepctr version [e.g. 0.8.3,]

.github/workflows/ci.yml

@@ -18,7 +18,7 @@ jobs:
     strategy:
       matrix:
         python-version: [3.6,3.7]
-        tf-version: [1.4.0,1.15.0,2.1.0,2.3.0]
+        tf-version: [1.4.0,1.15.0,2.1.0,2.4.0]
 
         exclude:
           - python-version: 3.7

CONTRIBUTING.md

@@ -1,10 +1,21 @@
 This project is under development and we need developers to participate in.
-
+# Join us
 If you
 
 - familiar with and interested in ctr prediction algorithms
 - familiar with tensorflow 
 - have spare time to learn and develop
 - familiar with git
 
-please send a brief introduction of your background and experience to [email protected], welcome to join us!
+please send a brief introduction of your background and experience to [email protected], welcome to join us!
+
+# Creating a pull request
+1. **Become a collaborator**: Send an email with introduction and your github account name to [email protected] and waiting for invitation to become a collaborator.
+2. **Fork&Dev**: Fork your own branch(`dev_yourname`) in `DeepCTR` from the `master` branch for development.If the `master` is updated during the development process, remember to merge and update to `dev_yourname` regularly.
+3. **Testing**: Test  logical correctness and effect when finishing the code development of the `dev_yourname` branch. 
+4. **Pre-release** : After testing contact [email protected] for pre-release integration, usually your branch `dev_yourname` will be merged into `release` branch by squash merge. 
+5. **Release a new version**: After confirming that the change is no longer needed, `release` branch will be merged into `master` and a new python package will be released on pypi.
+
+# Discussions
+
+https://github.com/shenweichen/DeepCTR/discussions

README.md

@@ -10,7 +10,7 @@
 
 
 [![Documentation Status](https://readthedocs.org/projects/deepctr-doc/badge/?version=latest)](https://deepctr-doc.readthedocs.io/)
-[![Build Status](https://travis-ci.org/shenweichen/DeepCTR.svg?branch=master)](https://travis-ci.org/shenweichen/DeepCTR)
+![CI status](https://github.com/shenweichen/deepctr/workflows/CI/badge.svg)
 [![Coverage Status](https://coveralls.io/repos/github/shenweichen/DeepCTR/badge.svg?branch=master)](https://coveralls.io/github/shenweichen/DeepCTR?branch=master)
 [![Codacy Badge](https://api.codacy.com/project/badge/Grade/d4099734dc0e4bab91d332ead8c0bdd0)](https://www.codacy.com/app/wcshen1994/DeepCTR?utm_source=github.com&utm_medium=referral&utm_content=shenweichen/DeepCTR&utm_campaign=Badge_Grade)
 [![Disscussion](https://img.shields.io/badge/chat-wechat-brightgreen?style=flat)](./README.md#disscussiongroup)
@@ -45,15 +45,16 @@ Let's [**Get Started!**](https://deepctr-doc.readthedocs.io/en/latest/Quick-Star
 |   Attentional Factorization Machine    | [IJCAI 2017][Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](http://www.ijcai.org/proceedings/2017/435) |
 |      Neural Factorization Machine      | [SIGIR 2017][Neural Factorization Machines for Sparse Predictive Analytics](https://arxiv.org/pdf/1708.05027.pdf)                                               |
 |                xDeepFM                 | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://arxiv.org/pdf/1803.05170.pdf)                         |
-|                AutoInt                 | [arxiv 2018][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/abs/1810.11921)                              |
-|         Deep Interest Network          | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1706.06978.pdf)                                                       |
+|         Deep Interest Network          | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1706.06978.pdf)     
+|                AutoInt                 | [CIKM 2019][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/abs/1810.11921)                              ||
 |    Deep Interest Evolution Network     | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1809.03672.pdf)                                            |
 |                FwFM                    | [WWW 2018][Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising](https://arxiv.org/pdf/1806.03514.pdf)                |
 |                  ONN                  | [arxiv 2019][Operation-aware Neural Networks for User Response Prediction](https://arxiv.org/pdf/1904.12579.pdf)                                                |
 |                 FGCNN                  | [WWW 2019][Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction ](https://arxiv.org/pdf/1904.04447)                             |
 |     Deep Session Interest Network      | [IJCAI 2019][Deep Session Interest Network for Click-Through Rate Prediction ](https://arxiv.org/abs/1905.06482)                                                |
 |                FiBiNET                 | [RecSys 2019][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.09433.pdf)   |
 |                FLEN                    | [arxiv 2019][FLEN: Leveraging Field for Scalable CTR Prediction](https://arxiv.org/pdf/1911.04690.pdf)   |
+|                DCN V2                    | [arxiv 2020][DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://arxiv.org/abs/2008.13535)   |
 
 ## Citation
 
@@ -75,7 +76,7 @@ If you find this code useful in your research, please cite it using the followin
 
 ## DisscussionGroup 交流群
 
-Please follow our wechat to join group:  
+- [Discussions](https://github.com/shenweichen/DeepCTR/discussions)
 - 公众号：**浅梦的学习笔记**  
 - wechat ID: **deepctrbot**
 

deepctr/__init__.py

@@ -1,4 +1,4 @@
 from .utils import check_version
 
-__version__ = '0.8.2'
+__version__ = '0.8.3'
 check_version(__version__)

deepctr/estimator/models/afm.py

@@ -2,7 +2,7 @@
 """
 
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Xiao J, Ye H, He X, et al. Attentional factorization machines: Learning the weight of feature interactions via attention networks[J]. arXiv preprint arXiv:1708.04617, 2017.

deepctr/estimator/models/autoint.py

@@ -2,7 +2,7 @@
 """
 
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Song W, Shi C, Xiao Z, et al. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks[J]. arXiv preprint arXiv:1810.11921, 2018.(https://arxiv.org/abs/1810.11921)

deepctr/estimator/models/ccpm.py

@@ -2,7 +2,7 @@
 """
 
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Liu Q, Yu F, Wu S, et al. A convolutional click prediction model[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, 2015: 1743-1746.

deepctr/estimator/models/dcn.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Wang R, Fu B, Fu G, et al. Deep & cross network for ad click predictions[C]//Proceedings of the ADKDD'17. ACM, 2017: 12. (https://arxiv.org/abs/1708.05123)

deepctr/estimator/models/deepfm.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Guo H, Tang R, Ye Y, et al. Deepfm: a factorization-machine based neural network for ctr prediction[J]. arXiv preprint arXiv:1703.04247, 2017.(https://arxiv.org/abs/1703.04247)

deepctr/estimator/models/fibinet.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Huang T, Zhang Z, Zhang J. FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1905.09433, 2019.

deepctr/estimator/models/fnn.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Zhang W, Du T, Wang J. Deep learning over multi-field categorical data[C]//European conference on information retrieval. Springer, Cham, 2016: 45-57.(https://arxiv.org/pdf/1601.02376.pdf)

deepctr/estimator/models/fwfm.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
     Harshit Pande
 
 Reference:

deepctr/estimator/models/nfm.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] He X, Chua T S. Neural factorization machines for sparse predictive analytics[C]//Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 2017: 355-364. (https://arxiv.org/abs/1708.05027)

deepctr/estimator/models/pnn.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Qu Y, Cai H, Ren K, et al. Product-based neural networks for user response prediction[C]//Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016: 1149-1154.(https://arxiv.org/pdf/1611.00144.pdf)

deepctr/estimator/models/wdl.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Cheng H T, Koc L, Harmsen J, et al. Wide & deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 2016: 7-10.(https://arxiv.org/pdf/1606.07792.pdf)

deepctr/estimator/models/xdeepfm.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Lian J, Zhou X, Zhang F, et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems[J]. arXiv preprint arXiv:1803.05170, 2018.(https://arxiv.org/pdf/1803.05170.pdf)

deepctr/models/afm.py

@@ -2,7 +2,7 @@
 """
 
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Xiao J, Ye H, He X, et al. Attentional factorization machines: Learning the weight of feature interactions via attention networks[J]. arXiv preprint arXiv:1708.04617, 2017.

deepctr/models/autoint.py

@@ -2,7 +2,7 @@
 """
 
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Song W, Shi C, Xiao Z, et al. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks[J]. arXiv preprint arXiv:1810.11921, 2018.(https://arxiv.org/abs/1810.11921)

deepctr/models/ccpm.py

@@ -2,7 +2,7 @@
 """
 
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Liu Q, Yu F, Wu S, et al. A convolutional click prediction model[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, 2015: 1743-1746.

deepctr/models/dcn.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
     Shuxun Zan, [email protected]
 

deepctr/models/dcnmix.py

@@ -1,14 +1,14 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
     Shuxun Zan, [email protected]
 
 Reference:
     [1] Wang R, Fu B, Fu G, et al. Deep & cross network for ad click predictions[C]//Proceedings of the ADKDD'17. ACM, 2017: 12. (https://arxiv.org/abs/1708.05123)
 
-    [2] Wang R, Shivanna R, Cheng D Z, et al. DCN-M: Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank Systems[J]. 2020. (https://arxiv.org/abs/2008.13535)
+    [2] Wang R, Shivanna R, Cheng D Z, et al. DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems[J]. 2020. (https://arxiv.org/abs/2008.13535)
 """
 import tensorflow as tf
 
@@ -22,7 +22,7 @@ def DCNMix(linear_feature_columns, dnn_feature_columns, cross_num=2,
            dnn_hidden_units=(128, 128,), l2_reg_linear=1e-5, l2_reg_embedding=1e-5, low_rank=32, num_experts=4,
            l2_reg_cross=1e-5, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_use_bn=False,
            dnn_activation='relu', task='binary'):
-    """Instantiates the Deep&Cross Network architecture.
+    """Instantiates the Deep&Cross Network with mixture of experts architecture.
 
     :param linear_feature_columns: An iterable containing all the features used by linear part of the model.
     :param dnn_feature_columns: An iterable containing all the features used by deep part of the model.

deepctr/models/deepfm.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Guo H, Tang R, Ye Y, et al. Deepfm: a factorization-machine based neural network for ctr prediction[J]. arXiv preprint arXiv:1703.04247, 2017.(https://arxiv.org/abs/1703.04247)

deepctr/models/dien.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Zhou G, Mou N, Fan Y, et al. Deep Interest Evolution Network for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1809.03672, 2018. (https://arxiv.org/pdf/1809.03672.pdf)

deepctr/models/din.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Zhou G, Zhu X, Song C, et al. Deep interest network for click-through rate prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018: 1059-1068. (https://arxiv.org/pdf/1706.06978.pdf)

deepctr/models/dsin.py

@@ -1,7 +1,7 @@
 # coding: utf-8
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Feng Y, Lv F, Shen W, et al. Deep Session Interest Network for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1905.06482, 2019.(https://arxiv.org/abs/1905.06482)

deepctr/models/fgcnn.py

@@ -2,7 +2,7 @@
 """
 
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Liu B, Tang R, Chen Y, et al. Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1904.04447, 2019.

deepctr/models/fibinet.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Huang T, Zhang Z, Zhang J. FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1905.09433, 2019.

deepctr/models/flen.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Tingyi Tan,[email protected]
+    Tingyi Tan, [email protected]
 
 Reference:
     [1] Chen W, Zhan L, Ci Y, Lin C. FLEN: Leveraging Field for Scalable CTR Prediction . arXiv preprint arXiv:1911.04690, 2019.(https://arxiv.org/pdf/1911.04690)

deepctr/models/fnn.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Zhang W, Du T, Wang J. Deep learning over multi-field categorical data[C]//European conference on information retrieval. Springer, Cham, 2016: 45-57.(https://arxiv.org/pdf/1601.02376.pdf)

deepctr/models/mlr.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Gai K, Zhu X, Li H, et al. Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction[J]. arXiv preprint arXiv:1704.05194, 2017.(https://arxiv.org/abs/1704.05194)

deepctr/models/nfm.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] He X, Chua T S. Neural factorization machines for sparse predictive analytics[C]//Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 2017: 355-364. (https://arxiv.org/abs/1708.05027)

deepctr/models/onn.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Yang Y, Xu B, Shen F, et al. Operation-aware Neural Networks for User Response Prediction[J]. arXiv preprint arXiv:1904.12579, 2019. （https://arxiv.org/pdf/1904.12579）

deepctr/models/pnn.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Qu Y, Cai H, Ren K, et al. Product-based neural networks for user response prediction[C]//Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016: 1149-1154.(https://arxiv.org/pdf/1611.00144.pdf)

deepctr/models/wdl.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Cheng H T, Koc L, Harmsen J, et al. Wide & deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 2016: 7-10.(https://arxiv.org/pdf/1606.07792.pdf)

deepctr/models/xdeepfm.py

@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 """
 Author:
-    Weichen Shen,[email protected]
+    Weichen Shen, [email protected]
 
 Reference:
     [1] Lian J, Zhou X, Zhang F, et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems[J]. arXiv preprint arXiv:1803.05170, 2018.(https://arxiv.org/pdf/1803.05170.pdf)