Skip to main content

A recommendation application using either item-based or user-based approaches

Project description


Recommender is a recommendation application using either item-based or user-based approaches.

Recommender is at version v0.3.0, also see change log for more details on release history.

If you like this project, feel fee to leave a few words of appreciation here Say Thanks!

Build Build Status Coverage Status
Quality Maintainability Requirements Status
Support gitter
Platform pyversion implementation

Table of contents

  1. Usage
  2. Contribution
  3. License

Usage

Install with pip

$ pip install recommender-engine

API

make_recommendation(person_to_recommend, preference_space, recommender_approach='user_based', number_of_items_to_recommend=10, similarity_measure='euclidean_distance')

	Return list of recommendation items based on the chosen approach and similarity emasure

	Parameters
	--------------
	person_to_recommend (str): user id/name to recommend to

	preference_space (dict):  keys are user id/name and values are dictionary of items and ratings

	recommender_approach (str): support 'user_based' (default) or 'item_based'

	number_of_items_to_recommend (int): number of items to recommend (default=10)

	similarity_measure (str): similarity measurement method , support 'euclidean_distance' (default), 'cosine' or 'pearson_correlation'

Example


>>> from recommender_engine import make_recommendation
>>>	result = make_recommendation(person_to_recommend = "userA",
								 preference_space = preference_space,
								 recommender_approach = 'user_based',
								 number_of_items_to_recommend = 10,
								 similarity_measure = 'euclidean_distance')

The preference space is dictionary data structure where keys are users and values are dictionary of items and ratings

preference_space = {
					'userA : {
							 'item1' : 'ratingA1, 
							 'item2' : 'ratingA2',
							  ..., 
							  'itemn' : 'ratingAn
							  }, 
					..., 
					'userZ:{
							'item1' : 'ratingZ1,
							 'item2' : 'ratingZ2',
							  ...,
							 'itemn' : 'ratingZn
							}
				    }

Tested Datasets

The project has been tested on these Datasets

  1. Jester
  2. MovieLens

Contribution Open Source Helpers

Please follow our contribution convention at contribution instruction and code of conduct

Please check out the issue file for list of issues that required helps.

Appreciation

Feel free to add your name into the list of contributors. You will automatically be inducted into Hall of Fame as a way to show my appreciation for your contributions

Hall of Fame


License

See the LICENSE file for license rights and limitations (Apache License 2.0).

Release history Release notifications

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for recommender-engine, version 0.3.0
Filename, size File type Python version Upload date Hashes
Filename, size recommender_engine-0.3.0-py2.py3-none-any.whl (16.4 kB) File type Wheel Python version py2.py3 Upload date Hashes View hashes
Filename, size recommender-engine-0.3.0.tar.gz (15.5 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page