How to Increase User Engagement and Click-Through Rate for Your Social Media App

Correct Answer: A.

Option A is correct.

Retraining your model with your initial training data plus new data representing new client products will keep the model in line with the product recommendation domain.

This will allow your model to recognize new products while retaining the knowledge of the foundational product data.

Option B is incorrect.

A regularization term will help prevent your model from overfitting, but it will not give your model the data it needs to recognize new product data.

Option C is incorrect.

Model drift happens when you receive new data on which to train and your model.

Changing your hyperparameters without retraining your model will not address your problem.

Option D is incorrect.

Completely recreating your model is not necessary and definitely not the most efficient way to address your model performance problem.

You will get better results more efficiently by adding new training data to your foundational training dataset and retraining your model.

References:

Please see the Amazon Machine Learning developer guide titled Training Parameters (https://docs.aws.amazon.com/machine-learning/latest/dg/training-parameters1.html),

The AWS News blog titled Amazon SageMaker Model Monitor - Fully Managed Automatic Monitoring For Your Machine Learning Models (https://aws.amazon.com/blogs/aws/amazon-sagemaker-model-monitor-fully-managed-automatic-monitoring-for-your-machine-learning-models/),

The Amazon SageMaker developer guide titled How Hyperparameter Tuning Works (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html)

The best and most efficient option to improve the user click-through rate for the social media app over time would be to choose option A, which is to periodically retrain the model using the foundational training data from the initial deployment and adding new data from new client products.

Retraining the model periodically helps to ensure that the model is up-to-date and can adapt to changes in the client product offerings. As new products are introduced or existing products change, the model needs to be updated to ensure that it continues to make accurate recommendations to users.

By retraining the model using foundational training data, the model can retain its original knowledge and continue to improve upon it with new data. This ensures that the model doesn't lose its effectiveness over time.

Adding new data from new client products helps the model to stay up-to-date and adapt to changes in the client product offerings. This ensures that the model can continue to make accurate recommendations to users, thereby improving the user click-through rate for the social media app over time.

Option B suggests periodically retraining the model from scratch using the foundational training data from the initial deployment and adding an L1 or L2 regularization value set to the higher range of the parameter to represent client product changes. However, this option may be inefficient and unnecessary since the model hasn't changed from its initial deployment.

Option C suggests periodically updating the model hyperparameters, setting the drift threshold to the higher range of the hyperparameter, to prevent model drift. However, this option may not be sufficient to improve the user click-through rate since it doesn't address the issue of the model's ability to adapt to changes in client product offerings.

Option D suggests completely recreating the model as it no longer recognizes client product changes. However, this option may not be efficient since it involves starting from scratch and may be time-consuming and costly.

Therefore, option A, which is to periodically retrain the model using the foundational training data from the initial deployment and adding new data from new client products, is the best and most efficient option to improve the user click-through rate for the social media app over time.