deepctr

v0.9.3

Easy-to-use,Modular and Extendible package of deep learning based CTR(Click Through Rate) prediction models with tensorflow 1.x and 2.x . For more information about how to use this package see README

Latest version published 1 year ago

License: Apache-2.0

PyPI

GitHub

Package Health Score

56 / 100

security
Security review needed
popularity
Popular
maintenance
Inactive
community
Sustainable

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Popularity

Popular

GitHub Stars: 7.34K
Forks: 2.17K
Contributors: 20

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package deepctr, we found that it has been starred 7,342 times.

Security

Security review needed

Security and license risk for latest version

Release Date: Nov 10, 2022
Direct Vulnerabilities: 0
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Indirect Vulnerabilities: 0
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License Risk: 0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

Apache-2.0

Security Policy

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Maintenance

Inactive

Commit Frequency

No Recent Commits

Open Issues: 90
Open PR: 12
Last Release: 1 year ago
Last Commit: 1 year ago

Further analysis of the maintenance status of deepctr based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

An important project maintenance signal to consider for deepctr is that it hasn't seen any new versions released to PyPI in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Sustainable

Readme: Yes
Contributing.md: Yes
Code of Conduct: No
Contributors: 20
Funding: No

With more than 10 contributors for the deepctr repository, this is possibly a sign for a growing and inviting community.

We found a way for you to contribute to the project! Looks like deepctr is missing a Code of Conduct.

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Package

Python Versions Compatibility: >=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*

Age: 5 years
Latest Release: 1 year ago
Dependencies: 2 Direct / 8 Total
Versions: 37
Maintainers: 1
Wheels: OS Independent

Not sure how to use deepctr?

To help you get started, we've collected the most common ways that deepctr is being used within popular public projects.

shenweichen / DeepCTR / deepctr / layers / interaction.py View on Github

if self.split_half:
                if idx != len(self.layer_size) - 1:
                    next_hidden, direct_connect = tf.split(
                        curr_out, 2 * [layer_size // 2], 1)
                else:
                    direct_connect = curr_out
                    next_hidden = 0
            else:
                direct_connect = curr_out
                next_hidden = curr_out

            final_result.append(direct_connect)
            hidden_nn_layers.append(next_hidden)

        result = tf.concat(final_result, axis=1)
        result = reduce_sum(result, -1, keep_dims=False)

        return result

View more ways to use deepctr

DeepCTR

CI status

DeepCTR is a Easy-to-use, Modular and Extendible package of deep-learning based CTR models along with lots of core components layers which can be used to easily build custom models.You can use any complex model with model.fit() ，and model.predict() .

Provide tf.keras.Model like interfaces for quick experiment. example
Provide tensorflow estimator interface for large scale data and distributed training. example
It is compatible with both tf 1.x and tf 2.x.

Some related projects:

DeepMatch: https://github.com/shenweichen/DeepMatch
DeepCTR-Torch: https://github.com/shenweichen/DeepCTR-Torch

Let's Get Started!(Chinese Introduction) and welcome to join us!

Models List

Model	Paper
Convolutional Click Prediction Model	[CIKM 2015]A Convolutional Click Prediction Model
Factorization-supported Neural Network	[ECIR 2016]Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction
Product-based Neural Network	[ICDM 2016]Product-based neural networks for user response prediction
Wide & Deep	[DLRS 2016]Wide & Deep Learning for Recommender Systems
DeepFM	[IJCAI 2017]DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Piece-wise Linear Model	[arxiv 2017]Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction
Deep & Cross Network	[ADKDD 2017]Deep & Cross Network for Ad Click Predictions
Attentional Factorization Machine	[IJCAI 2017]Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
Neural Factorization Machine	[SIGIR 2017]Neural Factorization Machines for Sparse Predictive Analytics
xDeepFM	[KDD 2018]xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
Deep Interest Network	[KDD 2018]Deep Interest Network for Click-Through Rate Prediction
AutoInt	[CIKM 2019]AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
Deep Interest Evolution Network	[AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction
FwFM	[WWW 2018]Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising
ONN	[arxiv 2019]Operation-aware Neural Networks for User Response Prediction
FGCNN	[WWW 2019]Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
Deep Session Interest Network	[IJCAI 2019]Deep Session Interest Network for Click-Through Rate Prediction
FiBiNET	[RecSys 2019]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
FLEN	[arxiv 2019]FLEN: Leveraging Field for Scalable CTR Prediction
BST	[DLP-KDD 2019]Behavior sequence transformer for e-commerce recommendation in Alibaba
IFM	[IJCAI 2019]An Input-aware Factorization Machine for Sparse Prediction
DCN V2	[arxiv 2020]DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
DIFM	[IJCAI 2020]A Dual Input-aware Factorization Machine for CTR Prediction
FEFM and DeepFEFM	[arxiv 2020]Field-Embedded Factorization Machines for Click-through rate prediction
SharedBottom	[arxiv 2017]An Overview of Multi-Task Learning in Deep Neural Networks
ESMM	[SIGIR 2018]Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate
MMOE	[KDD 2018]Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
PLE	[RecSys 2020]Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations
EDCN	[KDD 2021]Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models

Citation

Weichen Shen. (2017). DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models. https://github.com/shenweichen/deepctr.

If you find this code useful in your research, please cite it using the following BibTeX:

@misc{shen2017deepctr,
  author = {Weichen Shen},
  title = {DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models},
  year = {2017},
  publisher = {GitHub},
  journal = {GitHub Repository},
  howpublished = {\url{https://github.com/shenweichen/deepctr}},
}

DisscussionGroup

Github Discussions
Wechat Discussions

公众号：浅梦学习笔记	微信：deepctrbot	学习小组加入主题集合

Main contributors(welcome to join us!)

Shen Weichen

Alibaba Group

Zan Shuxun

Alibaba Group

Harshit Pande

Amazon

Lai Mincai

ByteDance

Li Zichao

ByteDance

Tan Tingyi

Chongqing University
of Posts and
Telecommunications

deepctr dependencies

h5py requests

FAQs

What is deepctr?

Easy-to-use,Modular and Extendible package of deep learning based CTR(Click Through Rate) prediction models with tensor... Visit Snyk Advisor to see a full health score report for deepctr, including popularity, security, maintenance & community analysis.

Is deepctr popular?

The python package deepctr receives a total of 12,331 weekly downloads. As such, deepctr popularity was classified as a popular. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is deepctr well maintained?

We found indications that deepctr is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is deepctr safe to use?

While scanning the latest version of deepctr, we found that a security review is needed. A total of 0 vulnerabilities or license issues were detected. See the full security scan results.

Last updated on 26 April-2024, at 23:09 (UTC).

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