old_contents: from recommender.recommender_engine import make_recommendation
  
  """
  	This sample used movielens datasets @ http://files.grouplens.org/datasets/movielens/ml-10m.zip
  """
  
  def make_preference_space_MovieLens(path):
  	""" create preference space for Movielens data set
  	"""
  	
  	movies = {}
  	for line in open(path + '/movies.dat'):
  		(movieid, title) = line.split('::')[0:2]
  		movies[movieid] = title
  	
  	preference_space = {}
  	for line in open(path + '/ratings.dat'):
  		(user, movieid, rating, ts) = line.split('::')
  		preference_space.setdefault(user, {})
  		preference_space[user][movies[movieid]] = float(rating)
  	
  	return preference_space
  
  
  def movielens():
  	""" Running the application using Movielens dataset
  	"""
  
  	#Creating preference_space from Movieslen dataset
  	preference_space = make_preference_space_MovieLens('../Movielens')
  
  	result =make_recommendation(person_to_recommend='1', 
                                 preference_space = preference_space, 
  							   recommender_approach = 'user_based', 
  							   number_of_items_to_recommend = 10,
  							   similarity_measure = 'euclidean_distance')
  	print(result)	
  
  if __name__=="__main__":
  	movielens()
new_contents: from recommender.recommender_engine import make_recommendation
  
  """
  	This sample used movielens datasets @ http://files.grouplens.org/datasets/movielens/ml-10m.zip
  """
  
  def make_preference_space_MovieLens(path):
  	""" create preference space for Movielens data set
  	"""
  	
  	movies = {}
  	for line in open(path + '/movies.dat'):
  		(movieid, title) = line.split('::')[0:2]
  		movies[movieid] = title
  	
  	preference_space = {}
  	for line in open(path + '/ratings.dat'):
  		(user, movieid, rating, ts) = line.split('::')
  		preference_space.setdefault(user, {})
  		preference_space[user][movies[movieid]] = float(rating)
  	
  	return preference_space
  
  
  def movielens():
  	""" Running the application using Movielens dataset
  	"""
  
  	#Creating preference_space from Movieslen dataset
  	preference_space = make_preference_space_MovieLens('../Movielens')
  
  	result = make_recommendation(person_to_recommend='1', 
                                 preference_space = preference_space, 
  							   recommender_approach = 'user_based', 
  							   number_of_items_to_recommend = 10,
  							   similarity_measure = 'euclidean_distance')
  	print(result)	
  
  if __name__=="__main__":
  	movielens()
subject: Fix type in recommendation