cbrecommender 1.0.0

A library for implementing Content-Based Recommendation Engines with ease!

cbrecommender

cbrecommender is a Python library for implementing Content-Based Recommendation Engines with ease!

Installation

Install from pypi with pip :

pip install cbrecommender

Usage

1. Importing and initializing :

from cbrecommender import CBRecommender

recommender = CBRecommender()

2. One Hot Encoding the features :

encoded_features = recommender.encode_features(features)

• features must be DataFrame that signifies the user's preferences. Example: movie genres, news topics, post tags etc.

• encoded_features() will return a OneHot-Encoded dataframe created from the supplied features.

3. Extracting user preferences and creating User-Profile :

user_profile = recommender.fit(train_features, scores)

• fit() is where we train our recommendation model and construct the user-profile.

• train_features must be a sample from encoded_features. Example: OneHot-Encoded genres of watched movies.

• scores must be an array denoting the user's preference (as measure) corresponding to each item of the selected sample. Example: Rating for a movie, song etc.

4. Get recommendations based on User-Profile :

recommendations = recommender.recommend(test_items, test_features, threshold_score, limit)

• test_items must be a pandas.DataFrame which denote those items that the user have not used for training. Example: Unwatched movies.

• test_features must be the OneHot-Encoded pandas.DataFrame of the features of the test_items.

• threshold_score must be numerical value (1-10) that specifies the threshold score for recommending items. Default is 7.5.

• limit must be an integer that denotes the number of items to recommended.

Example

from cbrecommender import CBRecommender
from pandas import DataFrame

data = 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(data)

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

recommender = CBRecommender()

# We are considering genre alone as the feature. You can include other features as well.
onehot_encoded_genres = recommender.encode_features(data[['genre']])
print(onehot_encoded_genres)

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

# Consider we had watched the first 4 movies. So we use it as training data to extract preferences.
# We use the user rating for the watched movies as the preference score.
watched_movie_genres = onehot_encoded_genres.iloc[:4, :]
watched_movie_ratings = [8.5,7.8,7.8,8.5]

user_profile = recommender.fit(watched_movie_genres, watched_movie_ratings)
print(recommender.user_profile)

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

# We use the remaining 4 unwatched movies as test data to get recommendations from.
unwatched_movies = data[['movie']].iloc[4:,:]
unwatched_movie_genres = onehot_encoded_genres.iloc[4:,:]

# Recommend top 3 movies with minimum expected rating of 5.0
recommendations = recommender.recommend(unwatched_movies, unwatched_movie_genres, 5.0, 3)
print(recommendations)

item	expected score
Jurassic World	6.45
Black Panther	6.45
The Last Jedi	6.37

Contributing

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

License

MIT License