If you were building Product Hunt, what is one feature you would love to add?

Here's how it could work: User Profiling: The system would create detailed user profiles based on their activity on the platform. It would take into account factors such as the types of products they've interacted with, the categories they're interested in, and any feedback they've provided. Collaborative Filtering: The recommendation engine would employ collaborative filtering techniques to identify users with similar tastes and preferences. By analyzing patterns of product engagement among similar users, it could suggest new products that a user might find interesting based on what similar users have liked. Content-Based Filtering: In addition to collaborative filtering, the system could also use content-based filtering methods. It would analyze the attributes of products (such as descriptions, tags, and categories) that a user has interacted with positively and recommend similar products. Real-Time Updates: The recommendation engine would continuously learn and adapt based on users' interactions and feedback. As users discover and engage with new products, the system would update their recommendations in real-time to ensure they receive the most relevant suggestions. Personalization Controls: To give users control over their experience, the platform would include settings where users can adjust the level of personalization they desire. They could fine-tune their preferences, specify categories of interest, or even opt-out of personalized recommendations altogether if they prefer a more manual browsing experience. Also have a look at https://civilidcheck.com/