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

Project description

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

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Table of contents

  1. Usage
  2. Contribution
  3. Project Architecture
  4. Release History
  5. Contact
  6. License


Install with pip

$ pip install recommender-engine


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

	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'


>>> from recommender_engine.recommender import make_recommendation
>>>	result = make_recommendation(person_to_recommend = "user1", 
								preference_space = preference_space,
								recommender = 'user_based', 
								number_of_items_to_recommend = 10,
								similarity = '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
							'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

List of issues

  1. Update unit test (#2)


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

Project Architecture

To do

Release History

  • v1.1.1 - Mar 17, 2019

    • Fix pypi shipping
  • v1.1.0 - Mar 17, 2019

    • Simplified code base
    • Added item-based approach
    • Published to pypi
  • v1.0.0 - Jan 16, 2018

    • First official release


Feel free to contact me to discuss any issues, questions, or comments.

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

Project details

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