Imagine a social media platform with millions of users and billions of posts. The platform wants to recommend posts to each user that they are most likely to engage with (like, comment, or share). However, simply recommending the most popular posts won't capture user individuality. Design an algorithm that balances popularity with user-specific preferences. The algorithm should consider factors like a user's past engagement history, the content of the post itself (text, images, videos), and the social circles of the user. Additionally, the algorithm should be efficient enough to handle the massive amount of data and deliver recommendations in real time.

What are the trade-offs involved in this design? How would you evaluate the success of such an algorithm?