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-2014007a-65cd-4296-bade-2e886e8fed52 100 0.1 10 3.0 13.368

Test RMSE for the best model is 10.02

Total time lapsed in data preprocessing and model training: 1.19 hours

Best Model saved in 0.08 mins

All the models trained in 1.18 hours

The following table gives information about all the models trained

model id rank lmbda iterations alpha RMSE
listenbrainz-recommendation-model-0de1e704-6090-4c2b-a80b-b46bb2951255 100 0.1 5 3.0 13.372
listenbrainz-recommendation-model-2014007a-65cd-4296-bade-2e886e8fed52 100 0.1 10 3.0 13.368
listenbrainz-recommendation-model-3a331f70-d081-4569-93f8-fc1c75a007da 100 10.0 5 3.0 13.402
listenbrainz-recommendation-model-043c2f5a-f157-4275-85a7-844eb78ec691 100 10.0 10 3.0 13.401
listenbrainz-recommendation-model-99ca4142-9903-482d-97e1-6f83b1a14a8f 120 0.1 5 3.0 13.372
listenbrainz-recommendation-model-4198b387-bba7-4c6c-aed4-c0117caa5bce 120 0.1 10 3.0 13.368
listenbrainz-recommendation-model-4d89f05a-7f06-442a-a00a-3bb43f1db5fd 120 10.0 5 3.0 13.402
listenbrainz-recommendation-model-1ad28fc1-f63f-419d-8b4a-a9187d360ec4 120 10.0 10 3.0 13.401

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.