To enhance the performance of the Siamese network in learning joint representations of item metadata and click data, several strategies can be implemented:
Feature Engineering: Utilize more advanced feature engineering techniques to extract meaningful information from item metadata. This could involve using more sophisticated text embedding methods, such as transformer-based models like BERT or RoBERTa, to capture semantic relationships in the metadata.
Data Augmentation: Increase the diversity and volume of training data by augmenting the existing dataset. This can help the Siamese network learn more robust and generalized representations by exposing it to a wider range of examples.
Regularization Techniques: Implement regularization techniques like dropout or batch normalization to prevent overfitting and improve the network's generalization capabilities.
Hyperparameter Tuning: Fine-tune the hyperparameters of the Siamese network, such as learning rate, batch size, and network architecture, to optimize its performance on the specific task of learning joint representations.
Ensemble Learning: Combine the outputs of multiple Siamese networks trained with different initializations or architectures to leverage the diversity of models and improve overall performance.
Transfer Learning: Pre-train the Siamese network on a related task or dataset with abundant data before fine-tuning it on the specific task of learning joint representations. This can help the network capture more intricate patterns in the data.