cbrecommender 0.0.2

Library for implementing Content-Based Recommendation System

cbrecommender

cbrecommender is a Python library for implementing a Content-Based Recommendation Engine.

Installation

Use the package manager pip to install cbrecommender.

pip install cbrecommender

Usage

# Import the class 'cbr' for Content-Based Recommender.
from cbrecommender import cbr

# Create the object.
r = cbr()

# OneHotEncode the features
r.encode_features(features)
''' 
* features(pandas.DataFrame) can be anything that signifies the user's preferences.
* For example, movie genres, news topics, post tags etc.
* Returns a OneHotEncoded dataframe from the features's comma (,) separated values.
'''

# Extract user's preferences and build the 'User-Profile'.
r.fit(features, scores)
''' 
* features(pandas.DataFrame) must be OneHotEncoded and is that of the items of user's choice.
* scores(list) denote the user's preference to the corresponding items.
* For example, it can be rating for a movie, song etc. 
* Returns the 'User-Profile', which is the model.
 '''
# Recommend items based on User-Profile.
r.recommend(items, features, [score, num])
''' 
* items(pandas.DataFrame) which denote those items that the user haven't chosen.
* features(pandas.DataFrame) is that of the items.
* score(float) is a non-mandatory parameter that specifies the threshold score for recommending items.
* num(int) is also a non-mandatory parameter that denotes the number of items to be recommended.
* Returns items along with their expected_score as a pandas.DataFrame object.
'''

Example

from cbrecommender import cbr
import pandas

df = pandas.DataFrame(
{'movie':['Endgame','Avatar','Titanic','Infinity War','Jurassic World','Black Panther',
          'Harry Potter-II','The Last Jedi'],
 'genre':['Action,Adventure,Drama','Action,Adventure,Fantasy','Drama,Romance',
          'Action,Adventure,Sci-Fi','Action,Adventure,Sci-Fi','Action,Adventure,Sci-Fi',
          'Adventure,Drama,Fantasy','Action,Adventure,Fantasy']
})
print(df)

movie	genre
Endgame	Action,Adventure,Drama
Avatar	Action,Adventure,Fantasy
Titanic	Drama,Romance
Infinity War	Action,Adventure,Sci-Fi
Jurassic World	Action,Adventure,Sci-Fi
Black Panther	Action,Adventure,Sci-Fi
Harry Potter-II	Adventure,Drama,Fantasy
The Last Jedi	Action,Adventure,Fantasy

r = cbr()
gen = r.encode_features(df.genre)
print(gen)

action	adventure	drama	fantasy	romance	sci-fi
1	1	1	0	0	0
1	1	0	1	0	0
0	0	1	0	1	0
1	1	0	0	0	1
1	1	0	0	0	1
1	1	0	0	0	1
0	1	1	1	0	0
1	1	0	1	0	0

rating = [8.5,7.8,7.8,8.5]
model = r.fit(gen.iloc[:4, :], rating)
print(r.user_profile)

action	adventure	drama	fantasy	romance	sci-fi
0.2755	0.2755	0.1811	0.0866	0.0866	0.0944

recommendations = r.recommend(df[['movie']].iloc[4:,:], gen.iloc[4:,:])
print(recommendations)

item	expected score
Jurassic World	6.45
Black Panther	6.45
Harry Potter-II	6.37
The Last Jedi	5.43

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT License