Design a recommendation engine for an e-commerce platform that can provide personalized recommendations for users based on their browsing and purchasing history, as well as other factors such as time of day and location.

When responding to this question, start with a high-level overview of how you would structure the recommendation engine, considering the scalability and responsiveness. Explain how you would collect and process user data, such as browsing and purchasing history, to generate personalized recommendations. Discuss the significance of using machine learning models, possibly collaborative filtering or deep learning, to predict user preferences accurately. Talk about how to incorporate real-time factors like time of day and location to make the recommendations more context-aware and precise. Address the importance of maintaining user privacy and adhering to data protection regulations while handling user data. Also, consider discussing the method of evaluating the performance and effectiveness of the recommendation engine, such as a/b testing or analyzing click-through rates. Keeping a focus on the user experience and the relevancy of recommendations throughout your explanation will be pivotal, along with mentioning the technical aspects of implementation.