Fairness-Aware_Tensor-Based_Recommendation | Ziwei Zhu , Xia Hu , and James Caverlee

by Zziwei Python Version: Current License: No License

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kandi X-RAY | Fairness-Aware_Tensor-Based_Recommendation Summary

Fairness-Aware_Tensor-Based_Recommendation is a Python library typically used in Institutions, Learning, Education applications. Fairness-Aware_Tensor-Based_Recommendation has no bugs, it has no vulnerabilities and it has low support. However Fairness-Aware_Tensor-Based_Recommendation build file is not available. You can download it from GitHub.

This is the implementation of our paper: Ziwei Zhu, Xia Hu, and James Caverlee. 2018. Fairness-Aware Tensor-Based Recommendation. In The 27th ACM International Conference on Information and Knowledge Management (CIKM ’18), October 22–26, 2018, Torino, Italy.

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Fairness-Aware_Tensor-Based_Recommendation has a low active ecosystem.

It has 6 star(s) with 1 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

Fairness-Aware_Tensor-Based_Recommendation has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Fairness-Aware_Tensor-Based_Recommendation is current.

Quality

Fairness-Aware_Tensor-Based_Recommendation has no bugs reported.

Security

Fairness-Aware_Tensor-Based_Recommendation has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Fairness-Aware_Tensor-Based_Recommendation does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

Fairness-Aware_Tensor-Based_Recommendation releases are not available. You will need to build from source code and install.

Fairness-Aware_Tensor-Based_Recommendation has no build file. You will be need to create the build yourself to build the component from source.

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Fairness-Aware_Tensor-Based_Recommendation Key Features

No Key Features are available at this moment for Fairness-Aware_Tensor-Based_Recommendation.

Fairness-Aware_Tensor-Based_Recommendation Examples and Code Snippets

No Code Snippets are available at this moment for Fairness-Aware_Tensor-Based_Recommendation.

Community Discussions

No Community Discussions are available at this moment for Fairness-Aware_Tensor-Based_Recommendation.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Fairness-Aware_Tensor-Based_Recommendation

You can download it from GitHub.
You can use Fairness-Aware_Tensor-Based_Recommendation like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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