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-6aeca548-fa9b-4b2b-a94b-03b325687b50 100 0.1 10 3.0 13.61

Test RMSE for the best model is 11.72

Total time lapsed in data preprocessing and model training: 1.01 hours

Best Model saved in 0.06 mins

All the models trained in 1.00 hours

The following table gives information about all the models trained

model id rank lmbda iterations alpha RMSE
listenbrainz-recommendation-model-a341b41b-193b-4822-8c47-2083131a17b8 100 0.1 5 3.0 13.614
listenbrainz-recommendation-model-6aeca548-fa9b-4b2b-a94b-03b325687b50 100 0.1 10 3.0 13.61
listenbrainz-recommendation-model-3400fcc8-8a18-46b2-8a96-2c70b1a3edbb 100 10.0 5 3.0 13.646
listenbrainz-recommendation-model-c50e2710-6de3-42ef-8da2-eba283e6696e 100 10.0 10 3.0 13.645
listenbrainz-recommendation-model-af4fdac8-2daa-4bf4-b2d3-0156085ed547 120 0.1 5 3.0 13.615
listenbrainz-recommendation-model-d1892c2d-eed2-498f-969b-d508fa35ec37 120 0.1 10 3.0 13.61
listenbrainz-recommendation-model-6a8a2364-ee17-4b2f-a3ef-3738dee6af0f 120 10.0 5 3.0 13.646
listenbrainz-recommendation-model-e4b4f606-ac7f-41a4-9515-07d7f9aa4cac 120 10.0 10 3.0 13.645

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.