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temporal_content_based_filtering.py
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temporal_content_based_filtering.py
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import pandas as pd
from sklearn.model_selection import train_test_split
from keras_recommender.library.content_based_filtering import TemporalContentBasedFiltering
def main():
data_dir_path = './data/ml-latest-small'
output_dir_path = './models'
records = pd.read_csv(data_dir_path + '/ratings.csv')
print(records.describe())
ratings_train, ratings_test = train_test_split(records, test_size=0.2, random_state=0)
timestamp_train = ratings_train['timestamp']
item_id_train = ratings_train['movieId']
rating_train = ratings_train['rating']
print(timestamp_train.head())
timestamp_test = ratings_test['timestamp']
item_id_test = ratings_test['movieId']
rating_test = ratings_test['rating']
max_item_id = records['movieId'].max()
config = dict()
config['max_item_id'] = max_item_id
cf = TemporalContentBasedFiltering()
history = cf.fit(config=config, timestamp_train=timestamp_train,
item_id_train=item_id_train,
rating_train=rating_train,
model_dir_path=output_dir_path)
metrics = cf.evaluate(timestamp_test=timestamp_test,
item_id_test=item_id_test,
rating_test=rating_test)
if __name__ == '__main__':
main()