KNNRecommenderTrain uses the input data to generate three model tables: the weights model ('ml_weights'), the bias model table ('ml_bias') and the optional nearest items or neighbors table ('ml_itemngbrs').
DROP TABLE IF EXISTS ml_weights;
DROP TABLE IF EXISTS ml_bias;
DROP TABLE IF EXISTS ml_itemngbrs;
SELECT iternum, rmse::VARCHAR(6) AS rmse FROM KnnRecommenderTrain (
ON (SELECT 1) PARTITION BY 1
RatingTable ('ml_ratings')
UserIdColumn ('userid')
ItemIdColumn ('itemid')
RatingColumn ('rating')
WeightModelTable ('ml_weights')
BiasModelTable ('ml_bias ')
NearestItemsTable ('ml_itemngbrs')
K (15)
MaxIterNum (20)
Threshold (0.0002)
LearningRate (0.001)
) ORDER BY iternum NULLS FIRST;