keras-recommender | Recommender built using keras | Recommender System library

by chen0040 Python Version: Current License: MIT

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

kandi X-RAY | keras-recommender Summary

keras-recommender is a Python library typically used in Artificial Intelligence, Recommender System, Deep Learning, Keras applications. keras-recommender has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Recommender built using keras

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Quality

Security

License

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Support

keras-recommender has a low active ecosystem.

It has 34 star(s) with 13 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. On average issues are closed in 446 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of keras-recommender is current.

Quality

keras-recommender has no bugs reported.

Security

keras-recommender has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

keras-recommender 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

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

Build file is available. You can 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 keras-recommender and discovered the below as its top functions. This is intended to give you an instant insight into keras-recommender implemented functionality, and help decide if they suit your requirements.

Fit model
Create the model
Fit the model
Fits the model
Download posters
Download movie from IMDB
Evaluate the model
Predict for item ids
Loads the model
Predict a single item
Predict for a single item
Loads the VGG16 model
Load the model
Predict for given items
Plots the history of a model
Creates the history plot
Evaluate the mean absolute error

Get all kandi verified functions for this library.

keras-recommender Key Features

No Key Features are available at this moment for keras-recommender.

keras-recommender Examples and Code Snippets

No Code Snippets are available at this moment for keras-recommender.

Community Discussions

Trending Discussions on keras-recommender

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 keras-recommender

You can download it from GitHub.
You can use keras-recommender 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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