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You are a machine learning engineer at a tech company responsible for maintaining a recommendation engine that drives personalized content for users. The current model has been performing well, but you’ve recently developed a new model that incorporates additional features and advanced techniques. Before fully replacing the existing model, you want to evaluate the new model's performance in a real-world environment. To do this, you decide to use A/B testing to compare the performance of the new model against the current model in terms of business outcomes. Which of the following approaches is the MOST EFFECTIVE for conducting A/B testing for this use case?