In this example, the MovieLens dataset is used. The column separator was changed to tab.
The file u_ordered.data is ordered by timestamp (this is not necessary but allows implementations that take advantage of temporal effects, e. g., concept drift). The file rfr.csv has a sample of the the users in u_ordered.data. Both files are attached.
#DEFINE rating_data_input_file ${PROJECTPATH}/ml-100k/u_ordered.data
#DEFINE rfr_data_input_file ${PROJECTPATH}/ml-100k/rfr.csv
#PARSER PQL
#RUNQUERY
rating_data := ACCESS({source='rating_data', wrapper='GenericPull', transport='File', protocol='CSV', datahandler='Tuple', options=[['Delimiter', '\t'],['filename', '${rating_data_input_file}']],schema=[['user','Integer'],['item','Integer'],['rating','Double'],['timestamp','StartTimeStamp']]})
#RUNQUERY
rfr := TIMEWINDOW({size = 1}, ACCESS({source='rfr', wrapper='GenericPull', transport='File', protocol='CSV', datahandler='Tuple', options=[['Delimiter', '\t'], ['filename', '${rfr_data_input_file}']], schema=[['user','Integer'], ['timestamp','StartTimeStamp']] } ) )
#QNAME RecommenderSystem
#QUERY
/// split learning and test data
splitted_rating_data = EXTRACT_TEST_DATA({strategy = 'ITTT'}, rating_data)
/// continuous learning
windowed_learning_data = TIMEWINDOW({size = [30, 'days']}, 0:splitted_rating_data)
models = TRAIN_RECSYS_MODEL({learner = 'BRISMF.MOA'}, windowed_learning_data)
/// recommending
recomm_candidates = RECOMMENDATION_CANDIDATES(JOIN(rfr, 1:models))
predicted_candidates = PREDICT_RATING(JOIN(models, recomm_candidates))
recommendations = RECOMMEND({top_n = 8, min_rating = 3.5}, predicted_candidates)
/// evaluation
predicted_test_data = PREDICT_RATING(JOIN(models, 1:splitted_rating_data))
model_errors = TEST_PREDICTION({aggregation_window_size = [24, 'hours']},
predicted_test_data)
|