Collaborative Filtering NN and CNN based recommender implemented with MXNet
The dataset is taken from ml-latest-small (MovieLens)
The trained models an be found in demo/models
- Collaborative Filtering V1: hidden factor analysis implementation of CF
- training: demo/collaborative_filtering_v1.py
- predicting: demo/collaborative_filtering_v1_predict.py
- Collaborative Filtering V2: CF with feedforward dense layer
- training: demo/collaborative_filtering_v2.py
- predicting: demo/collaborative_filtering_v2_predict.py
- Collaborative Filtering with Temporal Information: CF with feedforward dense layer and incorporate timestamp as input
- training: demo/collaborative_filtering_temporal.py
- predicting: demo/collaborative_filtering_temporal_predict.py
- Item-based Content-Based Filtering: Use timestamp information and item on content-based filtering
- trainng: demo/temporal_content_based_filtering.py
- predicting: demo/temporal_content_based_filtering_predict.py
The following code samples provide an illustration on both training and prediction using a deep learning model in the mxnet_recommender/library. Other deep learning models follow the similar training and prediction patterns.
To train a CF model, say CollaborativeFilteringV1, run the following commands:
pip install requirements.txt
cd demo
python collaborative_filtering_v1.py The training code in collaborative_filtering_v1.py is quite straightforward and illustrated below:
from sklearn.model_selection import train_test_split
import pandas as pd
from mxnet_recommender.library.cf import CollaborativeFilteringV1
data_dir_path = './data/ml-latest-small' # refers to demo/data/ml-latest-small folder
trained_model_dir_path = './models' # refers to demo/models folder
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)
user_id_train = ratings_train.as_matrix(columns=['userId'])
item_id_train = ratings_train.as_matrix(columns=['movieId'])
rating_train = ratings_train.as_matrix(columns=['rating'])
user_id_test = ratings_test.as_matrix(['userId'])
item_id_test = ratings_test.as_matrix(['movieId'])
rating_test = ratings_test.as_matrix(['rating'])
max_user_id = records['userId'].max()
max_item_id = records['movieId'].max()
# default context for the recommender is mxnet.cpu() which uses CPU for the model context and data context
# change this line to cf = CollaborativeFilteringV1(model_ctx=mxnet.gpu(0)) if you want to use GPU instead
cf = CollaborativeFilteringV1()
cf.max_user_id = max_user_id
cf.max_item_id = max_item_id
history = cf.fit(user_id_train=user_id_train,
item_id_train=item_id_train,
rating_train=rating_train,
model_dir_path=trained_model_dir_path)
metrics = cf.evaluate_mae(user_id_test=user_id_test,
item_id_test=item_id_test,
rating_test=rating_test)After the training is completed, the trained models will be saved as cf-v1-. in the demo/models.
To use the trained CF model to predict the rating of an item by a user, you can use the following code:
from mxnet_recommender.library.cf import CollaborativeFilteringV1
import pandas as pd
data_dir_path = './data/ml-latest-small' # refers to demo/data/ml-latest-small folder
trained_model_dir_path = './models' # refers to demo/models folder
records = pd.read_csv(data_dir_path + '/ratings.csv')
print(records.describe())
user_id_test = records['userId']
item_id_test = records['movieId']
cf = CollaborativeFilteringV1()
cf.load_model(trained_model_dir_path)
# batch prediction
predicted_ratings = cf.predict(user_id_test, item_id_test)
print(predicted_ratings)
# individual (user_id, item_id) prediction
for i in range(20):
user_id = user_id_test[i]
item_id = item_id_test[i]
predicted_rating = cf.predict_single(user_id, item_id)
print('predicted rating: ', predicted_rating)To train a CF model to also take timestamp into consideration, say CollaborativeFilteringWithTemporalInformation, run the following commands:
pip install requirements.txt
cd demo
python collaborative_filtering_temporal.py The training code in collaborative_filtering_temporal.py is illustrated below:
from sklearn.model_selection import train_test_split
import pandas as pd
from mxnet_recommender.library.cf import CollaborativeFilteringWithTemporalInformation
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.as_matrix(columns=['timestamp'])
user_id_train = ratings_train.as_matrix(columns=['userId'])
item_id_train = ratings_train.as_matrix(columns=['movieId'])
rating_train = ratings_train.as_matrix(columns=['rating'])
timestamp_test = ratings_test.as_matrix(columns=['timestamp'])
user_id_test = ratings_test.as_matrix(columns=['userId'])
item_id_test = ratings_test.as_matrix(columns=['movieId'])
rating_test = ratings_test.as_matrix(columns=['rating'])
max_user_id = records['userId'].max()
max_item_id = records['movieId'].max()
cf = CollaborativeFilteringWithTemporalInformation()
cf.max_user_id = max_user_id
cf.max_item_id = max_item_id
history = cf.fit(user_id_train=user_id_train,
item_id_train=item_id_train,
timestamp_train=timestamp_train,
rating_train=rating_train,
model_dir_path=output_dir_path,
epochs=20,
checkpoint_interval=2,
batch_size=256)
metrics = cf.evaluate_mae(user_id_test=user_id_test,
item_id_test=item_id_test,
timestamp_test=timestamp_test,
rating_test=rating_test)
if __name__ == '__main__':
main()
After the training is completed, the trained models will be saved as temporal-cf-. in the demo/models.
To use the trained CF model to predict the rating of an item by a user at a particular time, you can use the following code:
import pandas as pd
from mxnet_recommender.library.cf import CollaborativeFilteringWithTemporalInformation
def main():
data_dir_path = './data/ml-latest-small'
trained_model_dir_path = './models'
records = pd.read_csv(data_dir_path + '/ratings.csv')
print(records.describe())
timestamp_test = records.as_matrix(columns=['timestamp'])
user_id_test = records.as_matrix(columns=['userId'])
item_id_test = records.as_matrix(columns=['movieId'])
rating_test = records.as_matrix(columns=['rating'])
cf = CollaborativeFilteringWithTemporalInformation()
cf.load_model(model_dir_path=trained_model_dir_path)
predicted_ratings = cf.predict(user_id_test, item_id_test, timestamp_test)
print(predicted_ratings)
for i in range(20):
user_id = user_id_test[i]
item_id = item_id_test[i]
timestamp = timestamp_test[i]
rating = rating_test[i]
predicted_rating = cf.predict_single(user_id, item_id, timestamp)
print('predicted: ', predicted_rating, ' actual: ', rating)
if __name__ == '__main__':
main()To train a content-based filtering model, say TemporalContentBasedFiltering, run the following commands:
pip install requirements.txt
cd demo
python temporal_content_based_filtering.py The training code in temporal_content_based_filtering.py is illustrated below:
import pandas as pd
from sklearn.model_selection import train_test_split
from mxnet_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.as_matrix(columns=['timestamp'])
item_id_train = ratings_train.as_matrix(columns=['movieId'])
rating_train = ratings_train.as_matrix(columns=['rating'])
timestamp_test = ratings_test.as_matrix(columns=['timestamp'])
item_id_test = ratings_test.as_matrix(columns=['movieId'])
rating_test = ratings_test.as_matrix(columns=['rating'])
max_item_id = records['movieId'].max()
cf = TemporalContentBasedFiltering()
cf.max_item_id = max_item_id
history = cf.fit(timestamp_train=timestamp_train,
item_id_train=item_id_train,
rating_train=rating_train,
model_dir_path=output_dir_path,
epochs=20,
checkpoint_interval=2,
batch_size=256)
metrics = cf.evaluate_mae(timestamp_test=timestamp_test,
item_id_test=item_id_test,
rating_test=rating_test)
if __name__ == '__main__':
main()After the training is completed, the trained models will be saved as temporal-cbf-. in the demo/models.
To use the trained CF model to predict the rating of an item at a particular time, you can use the following code:
import pandas as pd
from mxnet_recommender.library.content_based_filtering import TemporalContentBasedFiltering
def main():
data_dir_path = './data/ml-latest-small'
trained_model_dir_path = './models'
records = pd.read_csv(data_dir_path + '/ratings.csv')
print(records.describe())
timestamp_test = records.as_matrix(columns=['timestamp'])
item_id_test = records.as_matrix(columns=['movieId'])
rating_test = records.as_matrix(columns=['rating'])
max_item_id = records['movieId'].max()
config = dict()
config['max_item_id'] = max_item_id
cf = TemporalContentBasedFiltering()
cf.load_model(model_dir_path=trained_model_dir_path)
predicted_ratings = cf.predict(item_id_test, timestamp_test)
print(predicted_ratings)
for i in range(20):
date = timestamp_test[i]
item_id = item_id_test[i]
rating = rating_test[i]
predicted_rating = cf.predict_single(item_id, date)
print('predicted: ', predicted_rating, ' actual: ', rating)
if __name__ == '__main__':
main()Note that the default training scripts in the demo folder use GPU for training, therefore, you must configure your graphic card for this (or remove the "model_ctx=mxnet.gpu(0)" in the training scripts).
- Step 1: Download and install the CUDA® Toolkit 9.0 (you should download CUDA® Toolkit 9.0)
- Step 2: Download and unzip the cuDNN 7.0.4 for CUDA@ Toolkit 9.0 and add the bin folder of the unzipped directory to the $PATH of your Windows environment
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