recommender-engine 0.3.0

Description:

recommenderengine 0.3.0

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.
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Table of contents

Usage
Contribution
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

Jester
MovieLens


Contribution
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).