GRCN | Refined Convolutional Network for Multimedia Recommendation
kandi X-RAY | GRCN Summary
kandi X-RAY | GRCN Summary
GRCN is a Python library typically used in User Interface, Pytorch applications. GRCN has no bugs, it has no vulnerabilities and it has low support. However GRCN build file is not available. You can download it from GitHub.
In this work, we focus on adaptively refining the structure of interaction graph to discover and prune potential false-positive edges. Towards this end, we devise a new GCN-based recommendermodel, Graph-Refined Convolutional Network(GRCN), which adjusts the structure of interaction graph adaptively based on status of mode training, instead of remaining the fixed structure.
In this work, we focus on adaptively refining the structure of interaction graph to discover and prune potential false-positive edges. Towards this end, we devise a new GCN-based recommendermodel, Graph-Refined Convolutional Network(GRCN), which adjusts the structure of interaction graph adaptively based on status of mode training, instead of remaining the fixed structure.
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GRCN has a low active ecosystem.
It has 5 star(s) with 3 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
GRCN has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of GRCN is current.
Quality
GRCN has no bugs reported.
Security
GRCN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
GRCN 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.
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GRCN releases are not available. You will need to build from source code and install.
GRCN has no build file. You will be need to create the build yourself to build the component from source.
Top functions reviewed by kandi - BETA
kandi has reviewed GRCN and discovered the below as its top functions. This is intended to give you an instant insight into GRCN implemented functionality, and help decide if they suit your requirements.
- Train the model
- Performs the forward computation
- Calculate the loss between two users
- Compute the full accuracy of a model
- Compute the full accuracy of each item
- Compute the tensorflow histogram
- Compute accuracy
- Loads data from a dataset
Get all kandi verified functions for this library.
GRCN Key Features
No Key Features are available at this moment for GRCN.
GRCN Examples and Code Snippets
No Code Snippets are available at this moment for GRCN.
Community Discussions
Trending Discussions on GRCN
QUESTION
Change the key of dictionary
Asked 2019-Sep-03 at 14:12
So I have the below dict
...ANSWER
Answered 2019-Sep-03 at 14:05Have you tried just adding a key, value pair to your dictionary for every number you need? For example:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install GRCN
You can download it from GitHub.
You can use GRCN 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.
You can use GRCN 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
This is our Pytorch implementation for the paper:. Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He and Tat-Seng Chua. Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback. In ACM MM`20, Seattle, United States, Oct. 12-16, 2020 Author: Dr. Yinwei Wei (weiyinwei at hotmail.com).
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