DeepRecommender | Deep learning for recommender systems | Recommender System library

by NVIDIA Python Version: Current License: MIT

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

kandi X-RAY | DeepRecommender Summary

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

Deep learning for recommender systems

Support

Quality

Security

License

Reuse

Support

DeepRecommender has a medium active ecosystem.

It has 1643 star(s) with 342 fork(s). There are 75 watchers for this library.

It had no major release in the last 6 months.

There are 10 open issues and 10 have been closed. On average issues are closed in 54 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of DeepRecommender is current.

Quality

DeepRecommender has 0 bugs and 33 code smells.

Security

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

DeepRecommender code analysis shows 0 unresolved vulnerabilities.

There are 2 security hotspots that need review.

License

DeepRecommender 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

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

DeepRecommender 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.

DeepRecommender saves you 399 person hours of effort in developing the same functionality from scratch.

It has 949 lines of code, 36 functions and 14 files.

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

Top functions reviewed by kandi - BETA

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

Return the decoded value
R Activation
Decodes the input using the encoder
Encodes a given tensor
Log variables and gradient summaries
Write a histogram summary
Writes a scalar summary
Evaluate the model
Iterate over one epoch
Helper function to create training data
Generate one epoch
Iterate a single epoch
Prints the stats
Saves data to a file
Add scalar summary

Get all kandi verified functions for this library.

DeepRecommender Key Features

No Key Features are available at this moment for DeepRecommender.

DeepRecommender Examples and Code Snippets

Exploration of Recommender Systems,Matrix Factorization Algorithms

Python

Lines of Code : 28

License : No License

Copy

Evaluating RMSE, MAE of algorithm KNNWithMeans on 5 split(s).

                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     
MAE (testset)     0.7191  0.7158  0.7138  0.7166  0.7254  0.7181  0.0040  
RMSE (testset)    0.9173  0.9162  0.9

Community Discussions

Trending Discussions on DeepRecommender

I'm facing issues with Data Preparation while using Netflix Data

QUESTION

I'm facing issues with Data Preparation while using Netflix Data

Asked 2021-Jan-27 at 15:47

I'm facing issues with Data Preparation while using Netflix Data. I just cloned a repo from Github and I'm facing issues while trying to run the code in Jupyter Notebook.

...

ANSWER

Answered 2021-Jan-27 at 15:47

I tried this and it worked fine.

Actually, I replaced $NF_PRIZE_DATASET with training_set (this is the folder under the root directory of DeepRecommender folder, training_set contains the dataset which I got from Netflix Dataset) and $NF_DATA with NF_DATA

Source https://stackoverflow.com/questions/65919017

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

Vulnerabilities

No vulnerabilities reported

Install DeepRecommender

You can download it from GitHub.
You can use DeepRecommender 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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