algorithms/itemBasedRecommender.py
def nearestNeighbors(item_id,items_and_sims,n):
    '''
    Sort the predictions list by similarity and select the top-N neighbors
    '''
    items_and_sims.sort(key=lambda x: x[1][0],reverse=True)
    return item_id, items_and_sims[:n]

    Sort the predictions list by similarity and select the top-N neighbors
    
Sort the predictions list by similarity and select the top-N neighbors
Sort the predictions list by similarity and select the top - N neighbors
def nearestNeighbors(item_id,items_and_sims,n):
    items_and_sims.sort(key=lambda x: x[1][0],reverse=True)
    return item_id, items_and_sims[:n]
def nearestNeighbors ( item_id , items_and_sims , n ) : items_and_sims . sort ( key = lambda x : x [ 1 ] [ 0 ] , reverse = True ) return item_id , items_and_sims [ : n ]
Sort the predictions list by similarity and select the top-N neighbors
Sort the predictions list by similarity and select the top - N neighbors
'''
    Sort the predictions list by similarity and select the top-N neighbors
    '''
item_id
items_and_sims
n