What is A/B testing? How to apply it in the context of model comparison?

A/B testing is a method of comparing two versions of a product or model to determine which one performs better based on specified metrics.

In the context of comparing machine learning models, A/B testing can be applied as follows:

• Divide users or data into two groups — A and B.
• Show the results of the first model to group A, and the second model to group B.
• Collect metrics (e.g., recommendation accuracy, conversion rate, response time).
• Analyze the results to understand which model is more effective.

This allows decision-making based on real data and user behavior, rather than solely on theoretical metrics.