Multi-Component-Graph-Convolutional-Collaborative-Filtering | Source code for AAAI 2020 paper

 by   BUPT-GAMMA Python Version: Current License: No License

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kandi X-RAY | Multi-Component-Graph-Convolutional-Collaborative-Filtering Summary

kandi X-RAY | Multi-Component-Graph-Convolutional-Collaborative-Filtering Summary

Multi-Component-Graph-Convolutional-Collaborative-Filtering is a Python library. Multi-Component-Graph-Convolutional-Collaborative-Filtering has no bugs, it has no vulnerabilities and it has low support. However Multi-Component-Graph-Convolutional-Collaborative-Filtering build file is not available. You can download it from GitHub.

Source code for AAAI 2020 paper "Multi-Component Graph Convolutional Collaborative Filtering"
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              Multi-Component-Graph-Convolutional-Collaborative-Filtering has a low active ecosystem.
              It has 60 star(s) with 16 fork(s). There are 2 watchers for this library.
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              It had no major release in the last 6 months.
              There are 3 open issues and 1 have been closed. On average issues are closed in 16 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of Multi-Component-Graph-Convolutional-Collaborative-Filtering is current.

            kandi-Quality Quality

              Multi-Component-Graph-Convolutional-Collaborative-Filtering has no bugs reported.

            kandi-Security Security

              Multi-Component-Graph-Convolutional-Collaborative-Filtering has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              Multi-Component-Graph-Convolutional-Collaborative-Filtering does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

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              Multi-Component-Graph-Convolutional-Collaborative-Filtering releases are not available. You will need to build from source code and install.
              Multi-Component-Graph-Convolutional-Collaborative-Filtering has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed Multi-Component-Graph-Convolutional-Collaborative-Filtering and discovered the below as its top functions. This is intended to give you an instant insight into Multi-Component-Graph-Convolutional-Collaborative-Filtering implemented functionality, and help decide if they suit your requirements.
            • Main function .
            • Initialize L0 .
            • Train the model .
            • Test the model .
            • Generate a resolver for a set of samples .
            • Performs the forward computation .
            • Calculate the loss of a network .
            • Calculate EMA .
            Get all kandi verified functions for this library.

            Multi-Component-Graph-Convolutional-Collaborative-Filtering Key Features

            No Key Features are available at this moment for Multi-Component-Graph-Convolutional-Collaborative-Filtering.

            Multi-Component-Graph-Convolutional-Collaborative-Filtering Examples and Code Snippets

            No Code Snippets are available at this moment for Multi-Component-Graph-Convolutional-Collaborative-Filtering.

            Community Discussions

            No Community Discussions are available at this moment for Multi-Component-Graph-Convolutional-Collaborative-Filtering.Refer to stack overflow page for discussions.

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

            Vulnerabilities

            No vulnerabilities reported

            Install Multi-Component-Graph-Convolutional-Collaborative-Filtering

            You can download it from GitHub.
            You can use Multi-Component-Graph-Convolutional-Collaborative-Filtering 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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