TF-recomm | Tensorflow-based Recommendation systems | Recommender System library

by songgc Python Version: Current License: Apache-2.0

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

kandi X-RAY | TF-recomm Summary

TF-recomm is a Python library typically used in Artificial Intelligence, Recommender System, Tensorflow applications. TF-recomm has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However TF-recomm build file is not available. You can download it from GitHub.

Tensorflow-based Recommendation systems

Support

Quality

Security

License

Reuse

Support

TF-recomm has a medium active ecosystem.

It has 906 star(s) with 227 fork(s). There are 68 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 8 have been closed. On average issues are closed in 12 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of TF-recomm is current.

Quality

TF-recomm has 0 bugs and 2 code smells.

Security

TF-recomm has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

TF-recomm code analysis shows 0 unresolved vulnerabilities.

There are 2 security hotspots that need review.

License

TF-recomm is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

TF-recomm releases are not available. You will need to build from source code and install.

TF-recomm 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.

TF-recomm saves you 58 person hours of effort in developing the same functionality from scratch.

It has 153 lines of code, 14 functions and 3 files.

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

Top functions reviewed by kandi - BETA

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

Compute SVD
Implementation of SVD
Calculate learning rate
Clip a array
Make a scalar summary
Get training and test data
Read process data from file

Get all kandi verified functions for this library.

TF-recomm Key Features

No Key Features are available at this moment for TF-recomm.

TF-recomm Examples and Code Snippets

No Code Snippets are available at this moment for TF-recomm.

Community Discussions

Trending Discussions on TF-recomm

Recommender System (SVD) with TensorFlow

QUESTION

Recommender System (SVD) with TensorFlow

Asked 2017-Jul-08 at 07:03

I'm trying to create a collaborative filtering algorithm to suggest products to certain users.

I started shortly and started working with TensorFlow (I thought it was sufficiently effective and flexible). I found this code that does what I'm interested in, creates the model and train the user IDs, products, and ratings: https://github.com/songgc/TF-recomm

I launched the code and trained the model.

After training the model I would need to make the predictions, that is, get suggestions for each user so that they can be saved in a DB from which I access with a NODE.js application.

How do I retrieve this list of suggestions for each user when the training is done?

...

ANSWER

Answered 2017-Jul-06 at 11:25

You can run

Source https://stackoverflow.com/questions/44898080

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

Vulnerabilities

No vulnerabilities reported

Install TF-recomm

You can download it from GitHub.
You can use TF-recomm 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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