TNN Model Overview

An TNN model is defined using TensorFlow's Keras API, featuring an Embedding layer to represent songs, an LSTM layer to capture sequential dependencies, and a Dense layer with a softmax activation to predict the next song. The model is compiled with the Adam optimizer and categorical crossentropy loss function. It is then trained on the training data for 10 epochs with a batch size of 32, while its performance is evaluated on the test set. Finally, the trained model is saved to the specified path for integration into a Flask application, ready to provide song recommendations based on user listening history.

Model Architecture Improvements

To enhance the model's performance, we have implemented the following improvements:

• Transformer-based Architecture: Replaced the LSTM with a MultiHeadAttention layer for better sequence modeling.
• Additional Features: Incorporated user metadata (e.g., age, gender) and song metadata (e.g., genre, artist) to enrich the model.
• Regularization: Added dropout layers and early stopping to prevent overfitting.
• Pre-trained Embeddings: Used pre-trained song embeddings to improve representation.

User Experience Enhancements

To provide a better user experience, we have implemented the following features:

• Personalization: Users can create profiles and specify their preferences (e.g., favorite genres, artists).
• Explainability: Recommendations include explanations like "Because you listened to [Song X]" or "Because you like [Genre Y]".
• Diversity: Ensures recommendations include a mix of genres and artists to avoid monotony.
• Real-Time Updates: Recommendations are updated in real-time based on the user's current listening session.

Evaluation Results

Test Loss	Test Accuracy	Manual Accuracy Score
4.7689	0.0385	0.0385

Evaluation Metrics

The model's performance is evaluated using the following metrics:

Test Loss

Test loss measures how well the model's predictions match the true labels in the test set. It is calculated using the categorical crossentropy loss function:

Where:

• N: Number of samples in the test set.
• C: Number of classes (e.g., number of unique songs).
• y_ij: True label (1 if sample i belongs to class j, otherwise 0).
• p_ij: Predicted probability that sample i belongs to class j.

Interpretation: A lower test loss indicates better model performance. If the test loss is high, the model is not generalizing well to the test data.

Test Accuracy

Test accuracy is the percentage of correct predictions made by the model on the test set. It is calculated as:

Interpretation: A higher test accuracy indicates better model performance. If the test accuracy is close to the training accuracy, the model is generalizing well. If the test accuracy is much lower than the training accuracy, the model may be overfitting.

Example

Suppose the model achieves a test loss of 0.25 and a test accuracy of 85%. This means:

• The model's predictions are, on average, 0.25 units away from the true labels (as measured by the loss function).
• The model correctly predicts 85% of the samples in the test set.

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