CollaborativeFiltering | simple c implementation of collaborative | Recommender System library

by bowbowbow C++ Version: Current License: MIT

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

kandi X-RAY | CollaborativeFiltering Summary

CollaborativeFiltering is a C++ library typically used in Artificial Intelligence, Recommender System applications. CollaborativeFiltering has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

simple c++ implementation of collaborative filtering.

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Support

CollaborativeFiltering has a low active ecosystem.

It has 16 star(s) with 3 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of CollaborativeFiltering is current.

Quality

CollaborativeFiltering has no bugs reported.

Security

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

License

CollaborativeFiltering 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

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

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CollaborativeFiltering Key Features

No Key Features are available at this moment for CollaborativeFiltering.

CollaborativeFiltering Examples and Code Snippets

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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.

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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