NNCF | Sampling Strategies for Neural Network | Recommender System library

by chentingpc Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | NNCF Summary

NNCF is a Python library typically used in Artificial Intelligence, Recommender System, Deep Learning, Pytorch applications. NNCF has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However NNCF build file is not available. You can download it from GitHub.

This repository contains code for paper "On Sampling Strategies for Neural Network-based Collaborative Filtering", which propose (1) a general NNCF framework incorporates both interaction and content information, and (2) sampling strategies for speed up the process.

Support

Quality

Security

License

Reuse

Support

NNCF has a low active ecosystem.

It has 34 star(s) with 17 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. On average issues are closed in 1 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of NNCF is current.

Quality

NNCF has 0 bugs and 0 code smells.

Security

NNCF has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

NNCF code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

NNCF is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

NNCF releases are not available. You will need to build from source code and install.

NNCF has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

NNCF saves you 1622 person hours of effort in developing the same functionality from scratch.

It has 3602 lines of code, 188 functions and 35 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed NNCF and discovered the below as its top functions. This is intended to give you an instant insight into NNCF implemented functionality, and help decide if they suit your requirements.

Creates a model
Set the form
Call the function
Normalize shape
Approximation of the K - Means
Compute the APK score
Get the conf from the given data_name
Return a Conf object for the given data_name
Generate a Conf object for the given data_name
Create a Conf object
Get the base seed and variable

Get all kandi verified functions for this library.

NNCF Key Features

No Key Features are available at this moment for NNCF.

NNCF Examples and Code Snippets

No Code Snippets are available at this moment for NNCF.

Community Discussions

Trending Discussions on NNCF

why before embedding, have to make the item be sequential starting at zero

QUESTION

why before embedding, have to make the item be sequential starting at zero

Asked 2020-Mar-14 at 14:13

I learn collaborative filtering from this bolg, Deep Learning With Keras: Recommender Systems.

The tutorial is good, and the code working well. Here is my code.

There is one thing confuse me, the author said,

The user/movie fields are currently non-sequential integers representing some unique ID for that entity. We need them to be sequential starting at zero to use for modeling (you'll see why later).

...

ANSWER

Answered 2020-Mar-14 at 14:13

Embeddings are assumed to be sequential.

The first input of Embedding is the input dimension. So, if the input exceeds the input dimension the value is ignored. Embedding assumes that max value in the input is input dimension -1 (it starts from 0).

https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding?hl=ja

As an example, the following code will generate embeddings only for input [4,3] and will skip the input [7, 8] since input dimension is 5.

I think it is more clear to explain it with tensorflow;

Source https://stackoverflow.com/questions/60341662

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

Vulnerabilities

No vulnerabilities reported

Install NNCF

unzip data in ./data folder, and go to ./code/sampler, execute ./make.sh
run using scripts under ./code/scripts/demos, which are prepared for each of the sampling strategies.
after running, the results are stored in ./results folder

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 .

Find more information at: