CollaborativeFiltering | Collabrative Filtering way of recommendation | Recommender System library

by saimadhu-polamuri Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | CollaborativeFiltering Summary

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

Implementation of Collabrative Filtering way of recommendation engine.

Support

Quality

Security

License

Reuse

Support

CollaborativeFiltering has a low active ecosystem.

It has 76 star(s) with 73 fork(s). There are 12 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 1495 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of CollaborativeFiltering is current.

Quality

CollaborativeFiltering has 0 bugs and 0 code smells.

Security

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

CollaborativeFiltering code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

CollaborativeFiltering 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.

Reuse

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

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

CollaborativeFiltering saves you 36 person hours of effort in developing the same functionality from scratch.

It has 98 lines of code, 4 functions and 2 files.

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

Top functions reviewed by kandi - BETA

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

Get a list of recommended recommendations for a person .
Calculate Pearson correlation coefficient .
Compute Euclidean distance between two people .
Return the number of most similar people for a given person .

Get all kandi verified functions for this library.

CollaborativeFiltering Key Features

No Key Features are available at this moment for CollaborativeFiltering.

CollaborativeFiltering Examples and Code Snippets

No Code Snippets are available at this moment for CollaborativeFiltering.

Community Discussions

Trending Discussions on CollaborativeFiltering

Declaring path in pycharm not working

Firebase Cloud Function does not work while using Likely module

In Spark: MatrixFactorizationModel.scala “recommendProductsForUsers” function takes very long time to complete

QUESTION

Declaring path in pycharm not working

Asked 2018-Apr-21 at 20:05

I am using Pycharm and I created the project in a folder called collaborative filtering. I have some csv in a folder called ml-latest-small that I also placed in the collaborative filtering folder that has the .py file I am working from.

I am getting the following errors:

...

ANSWER

Answered 2018-Apr-21 at 19:32

Here, the ~ means $HOME (read here):

which is why you end up with: /Users//Users/usernamehere/Desktop/Machine Learning/Lesson 5/ratings.csv' which is not a valid path.

Source https://stackoverflow.com/questions/49959335

QUESTION

Firebase Cloud Function does not work while using Likely module

Asked 2018-Apr-01 at 17:01

Our Android project uses Firebase Database, and we want to use Firebase Cloud Functions for generating a recommendation for the user. For this, we decided to use npm likely module. Here is my function in index.js:

...

ANSWER

Answered 2018-Apr-01 at 17:01

For database triggers must return a promise that resolves when all the asynchronous work in the function is complete. once() is async and does not block the function, so it returns immediately before the data is available to the callback. You should be using the promise returned from once() (and not the callback function parameter) to respond when the data is available.

If you don't return a promise that resolves when all the work is complete, Cloud Functions may clean up your function stop all of its work before it's complete.

Source https://stackoverflow.com/questions/49598056

QUESTION

In Spark: MatrixFactorizationModel.scala “recommendProductsForUsers” function takes very long time to complete

Asked 2017-Apr-21 at 00:10

I have 9 nodes cluster and each node has the following configurations,

I’m trying to generate recommendations for all the users in MatrixFactorizationModel using 'recommendProductsForUsers' function. Looks like it takes very long time to complete (eg: For 1 month of data it takes approximately around 34 hours). Is it due to the iteration for multiple times over the matrix?

How can I reduce the execution time?

These are my spark-submit configuration:

spark-submit --jars $JAR_LOC --class com.collaborativefiltering.CustomerCollaborativeJob --driver-memory 5G --num-executors 7 --executor-cores 2 --executor-memory 20G --master yarn-client cust_rec/cust-rec.jar --period 1month --out /PATH --rank 50 --numIterations 2 --lambda 0.25 --alpha 300 --topK 20

Thank you very much in advance.

...

ANSWER

Answered 2017-Apr-21 at 00:10

I found in MatrixFactorizationModel the recommendProductsForUsers runs through multiple iteration so the computational time is high. Once I started to run my jobs in cloud, I tested the job by increasing the nodes and spark-executors. It actually worked! I was able to run and complete the job within 4 hours.

Source https://stackoverflow.com/questions/41424332

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

Vulnerabilities

No vulnerabilities reported

Install CollaborativeFiltering

You can download it from GitHub.
You can use CollaborativeFiltering 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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