[Fixed]-A good collaborative filtering/matching/recommendation library for Python/Django?

by

2👍

✅

They covered this subject in the free Stanford ML class. Check the videos for chapter XVI at http://www.ml-class.org/course/video/preview_list

Although the implementation discussed is Matlab/Octave it should be not difficult to implement in Python, even easier if you are using Numpy

👤Josep Valls

0👍

There are some good books out there on the subject of social media combined with Python.

👤Jonas Geiregat

0👍

A very flexible solution that works in any coding language (including Python) is the Abracadabra Recommender API.

Basically it is a Recommender Algorithms as a Service library. The setup is very straightforward: you only need to send HTTP calls (which you can do with Django) to the API endpoint url to train your model and to receive recommendations. View the docs how.

With the Abracadabra Recommender API, when using Python, you first add data to your model:

# These code snippets use an open-source library. http://unirest.io/python
response = unirest.post("https://noodlio-abracadabra-recommender-systems-v1.p.mashape.com/add/subjects?recommenderId=rec1&subjectId=See+docs",
  headers={
    "X-Mashape-Key": "<required>",
    "Accept": "application/json",
    "Content-Type": "application/json"
  }
)

Then you train the model by rating or liking subjects (for instance movies):

# These code snippets use an open-source library. http://unirest.io/python
response = unirest.post("https://noodlio-abracadabra-recommender-systems-v1.p.mashape.com/rate/subject?recommenderId=rec1&subjectId=gameofthrones&subjectWeight=10&userId=user1",
  headers={
    "X-Mashape-Key": "<required>",
    "Accept": "application/json",
    "Content-Type": "application/json"
  }
)

Once done, then you receive recommendations based on Content-Based, Collaborative or Hybrid filtering as follows:

# These code snippets use an open-source library. http://unirest.io/python
response = unirest.post("https://noodlio-abracadabra-recommender-systems-v1.p.mashape.com/recommend?method=content&recommenderId=rec1&userId=user1",
  headers={
    "X-Mashape-Key": "<required>",
    "Accept": "application/json",
    "Content-Type": "application/json"
  }
)

You can view more example in other languages, including Angular, React, Javascript, NodeJS, Curl, Java, Python, Objective-C, Ruby, .NET… on the API homepage.

👤WJA

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