$ pip install recommender-engine

	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'

>>> 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')

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