Sparks's inbuilt function to train a model takes a dataframe of 'implicit preferences' given by users to some products. userID ~ user_id, productID ~ recording_id and preference ~ transformed count as represented by rows in playcounts dataframe.
Playcounts dataframe is loaded from HDFS in 0.00 mins and is of the form:
user_id | recording_id | playcount | transformed_listencount |
---|
Here, playcount is the actual listen count whereas the transformed_listencount was calculated by applying the following function on playcounts.
No transformation applied to listen counts
Preprocessing of playcounts dataframe takes 0.00 mins. The preprocessed data divided the data into training and test data, in 5 : 1. The model was then trained on the training data using K-Folds Cross Validation with 5 folds. From the models trained, the one with least RMSE is selected to generate recommendations.
Of all the trained models, the model with the least RMSE value is chosen to generate recommendations. The model will be referred to as "best model".
Best model parameters are as follows:
Best model ID | Best model rank | Best model lmbda | Best model iteration | Best model alpha | Best model RMSE |
---|---|---|---|---|---|
listenbrainz-recommendation-model-890ad52d-08d2-4f2c-9439-17ec175ca22c | 120 | 0.1 | 10 | 3.0 | 11.293 |
Test RMSE for the best model is 17.48
Total time lapsed in data preprocessing and model training: 3.86 hours
Best Model saved in 0.18 mins
All the models trained in 3.85 hours
The following table gives information about all the models trained
model id | rank | lmbda | iterations | alpha | RMSE |
---|---|---|---|---|---|
listenbrainz-recommendation-model-603b124a-6301-41f4-b2c5-9247fcc8275b | 100 | 0.1 | 5 | 3.0 | 11.298 |
listenbrainz-recommendation-model-727d99b0-7996-451a-bbbf-6ecebe8f7ed4 | 100 | 0.1 | 10 | 3.0 | 11.293 |
listenbrainz-recommendation-model-9e319d40-a188-4609-9a4b-e512a5cfc95f | 100 | 10.0 | 5 | 3.0 | 11.343 |
listenbrainz-recommendation-model-b5a2a046-9cc3-4415-a15a-22c6800bd4f2 | 100 | 10.0 | 10 | 3.0 | 11.342 |
listenbrainz-recommendation-model-37e2cef7-2cc9-486a-a05c-acefe43f0357 | 120 | 0.1 | 5 | 3.0 | 11.298 |
listenbrainz-recommendation-model-890ad52d-08d2-4f2c-9439-17ec175ca22c | 120 | 0.1 | 10 | 3.0 | 11.293 |
listenbrainz-recommendation-model-b54ccce1-1ec5-43db-b6ae-08e793292624 | 120 | 10.0 | 5 | 3.0 | 11.343 |
listenbrainz-recommendation-model-dd9b9f02-d0cb-4f80-a357-5bef54845550 | 120 | 10.0 | 10 | 3.0 | 11.342 |
The Mean Squared Error (MSE) is a direct measure of the reconstruction error of the user-item rating matrix. It is defined as the sum of the squared errors divided by the number of observations. The squared error, in turn, is the square of the difference between the predicted rating for a given user-item pair and the actual rating.
Ratings are predicted for all the (user_id, recording_id) pairs in validation data, the predicted ratings are then subtracted with actual ratings and RMSE is calculated.
Note: Number of rows in a dataframe or number of elements in a dataframe (count information) is not included because it leads to unnecessary computation time.