Recommender

Usage

recommendation_system.Recomemder(dataset)

from recommendation_system import Recommender

# given a dataset
dataset = [['itemC', 'itemB', 'itemE'],
           ['itemE', 'itemG','itemA','itemB', 'itemD'，...],
           ...]

# new a Recommender object        
rs = Recommender(dataset)

# recommend user_0 5 items accroding to the most popular
popular = rs.popular(dataset[0], n=5)

# recommend user_1 10 items accroding to the user-based CF
user_based = rs.user_based(1)[:10]

# recommend user_2 some items accroding to the item-based CF
item_based = rs.item_based(2)

Parameter

dataset:

list of list

Attributes

dataset

list of list, the inner list is the items which a user interests

unique

the union of all items

usr_matrix

the similarity matrix of user to user

item_matrix

the similarity matrix of item to item

Methods

popular(data, n=None)

recommend items according to the most popular

parameter

data: []

n: number of recommending items

return

tuple of list

user_based(subset, include_current_items=False)

recommend items according to user-based collaborative filtering

parameter

subset: int, the index of dataset

include_current_items: bool

return

tuple of list

item_based(subset, include_current_items=False)

recommend items according to item-based collaborative filtering

parameter

subset: int, the index of dataset

include_current_items: bool

return

tuple of list

most_similar_set_to(subset)

recommend items to

parameter

subset: int, index of dataset

return

tuple of list, [(subset_i, similarity), ...]

most_similar_item_to(item_id)

recommend items to

parameter

item_id: int, index of item

return

tuple of list, [(item_i, similarity), ...]