Data preprocessing and model training

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 2 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-7e7f2f72-87af-49ad-a9d9-8a760e891cef 100 0.1 10 3.0 12.463

Test RMSE for the best model is 15.903

Total time lapsed in data preprocessing and model training: 1.24 hours

Best Model saved in 0.12 mins

All the models trained in 1.24 hours

The following table gives information about all the models trained

model id rank lmbda iterations alpha RMSE
listenbrainz-recommendation-model-d2f7bdb9-20a2-43b7-8775-6b9b3c1b90fe 100 0.1 5 3.0 12.468
listenbrainz-recommendation-model-7e7f2f72-87af-49ad-a9d9-8a760e891cef 100 0.1 10 3.0 12.463
listenbrainz-recommendation-model-97c4c7a4-84b8-43ee-ae08-45f892ec2582 100 10.0 5 3.0 12.5
listenbrainz-recommendation-model-40b798bc-9555-4e37-8a59-3ad897a7bc19 100 10.0 10 3.0 12.5
listenbrainz-recommendation-model-c484d67f-cd6d-4951-980e-e03b31dc0655 120 0.1 5 3.0 12.468
listenbrainz-recommendation-model-be8236cf-9138-4869-9713-b7e156c85895 120 0.1 10 3.0 12.463
listenbrainz-recommendation-model-9e20191f-010b-4bbb-b2b7-6408d17fe8b0 120 10.0 5 3.0 12.5
listenbrainz-recommendation-model-d417d824-bc77-4ae1-9cba-5eb08690b215 120 10.0 10 3.0 12.5

Following are the parameters required to train the model

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.