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

While I greatly respect and appreciate the work done by the Product Hunt team, if given the opportunity, one feature I would love to explore adding is a more robust recommendation engine. Enhancing the platform's ability to curate personalized product recommendations based on users' interests and past interactions could greatly enhance the discovery and user experience on Product Hunt. However, I understand that the Product Hunt team continuously strives to improve and innovate, and I trust their expertise in deciding which features best align with their vision and goals.

Direct message

* I would love to be notified every time somebody follows the teaser page. I am obsessed with the follower count on that page at the moment, but have to visit it ten times a day to see if any new followers have come in 🙈 * There is no way for me to know who has followed me on the web / desktop version. I can only figure that through the mobile app. Which is honestly not that big a deal, but would be good to have since I prefer browsing PH on my laptop (I avoid using my phone while im working as much as i can)

Dark mode.

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/