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 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-70633358-80f5-44dc-a394-92d7d602b484 120 0.1 10 3.0 11.606

Test RMSE for the best model is 16.15

Total time lapsed in data preprocessing and model training: 3.80 hours

Best Model saved in 0.17 mins

All the models trained in 3.80 hours

The following table gives information about all the models trained

model id rank lmbda iterations alpha RMSE
listenbrainz-recommendation-model-396d906b-8aeb-468b-bd85-bae9854c8ef8 100 0.1 5 3.0 11.611
listenbrainz-recommendation-model-887f5b49-401a-4af2-8725-e5f82134b974 100 0.1 10 3.0 11.606
listenbrainz-recommendation-model-a93aff09-ddf9-4d3b-ac21-aecb9111d232 100 10.0 5 3.0 11.657
listenbrainz-recommendation-model-a6357e2a-7b4e-41c4-bc70-ce3d2fa9d928 100 10.0 10 3.0 11.656
listenbrainz-recommendation-model-2ff0840b-2fb9-4b9f-848b-d7895d8a1897 120 0.1 5 3.0 11.612
listenbrainz-recommendation-model-70633358-80f5-44dc-a394-92d7d602b484 120 0.1 10 3.0 11.606
listenbrainz-recommendation-model-eb2859f8-a884-48ef-a50c-30f2af71d93e 120 10.0 5 3.0 11.657
listenbrainz-recommendation-model-aaec4991-e390-47c7-8555-862d82063dc7 120 10.0 10 3.0 11.656

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.