At RecomendeMe, we believe a single recommendation can change your day β or even how you see the world. This idea inspired the creation of our Cultural Feed, a new way to showcase recommendations for movies, books, music, podcasts, events, or any cultural expression that makes sense for each person.
π― What problem were we solving?
When I first designed RecomendeMe, I noticed that most recommendation platforms treat content as generic β buried in endless lists or algorithmic carousels. I wanted the opposite: every cultural recommendation should stand out like a magazine cover, not get lost in the noise.
π§© What is the Cultural Feed?
The Cultural Feed is the dynamic heart of RecomendeMe.
- Each recommendation has its own space, like a feature cover.
- The design highlights cover images, short synopses, and direct links to streaming platforms, bookstores, or playlists.
- The feed is personalized based on each userβs tastes and interactions β the more you like, recommend, and explore, the more relevant your feed becomes.
βοΈ How did we build it?
From a technical perspective:
Backend: We use PHP and MySQL to manage tables for recommendations, users, likes, and comments.
Frontend: We built the feed with HTML, Tailwind CSS, and JavaScript to deliver a fast, responsive experience on both desktop and mobile.
Each recommendation is fetched dynamically based on interest filters (e.g., genre, preferred platform, interaction history).
π What makes it different?
Unlike generic feeds, here every item is treated as a highlight. When a user shares a movie, book, or album, it doesnβt disappear into the void β it gets real visibility, and can even become a trend within the community.
β¨ Why share this on Dev.to?
Iβm sharing this here because I believe cultural products and tech can work together in smarter, more human ways. If youβre building recommendation systems, social feeds, or content platforms, I hope this sparks some ideas β or at least a good conversation.
If youβd like to learn more, share thoughts, or even contribute: letβs connect!
π RecomendeMe β where every recommendation counts.
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