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-e528d3e4-89c0-4f0f-a992-aa70e5c5b230 120 0.1 10 3.0 11.837

Test RMSE for the best model is 10.883

Total time lapsed in data preprocessing and model training: 3.73 hours

Best Model saved in 0.16 mins

All the models trained in 3.72 hours

The following table gives information about all the models trained

model id rank lmbda iterations alpha RMSE
listenbrainz-recommendation-model-5a26aefb-3416-47bf-be7e-081b62c7821b 100 0.1 5 3.0 11.842
listenbrainz-recommendation-model-245f0cfd-9366-4ad2-9e04-9074f9ce0315 100 0.1 10 3.0 11.837
listenbrainz-recommendation-model-dbb545d9-e6bf-4378-9791-b2cc904df590 100 10.0 5 3.0 11.885
listenbrainz-recommendation-model-742f2305-892d-44bf-88dd-34fd62290d5e 100 10.0 10 3.0 11.885
listenbrainz-recommendation-model-0bfc96e2-8c13-40f3-b3ee-0978fb2a3fe4 120 0.1 5 3.0 11.842
listenbrainz-recommendation-model-e528d3e4-89c0-4f0f-a992-aa70e5c5b230 120 0.1 10 3.0 11.837
listenbrainz-recommendation-model-73c4b2b8-ee0d-427e-9d9a-c47e8e39b165 120 10.0 5 3.0 11.885
listenbrainz-recommendation-model-b5d060cf-9197-4764-83cd-62b3ba6bb767 120 10.0 10 3.0 11.885

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.