AI Movie Recommendation & OTT Aggregator -- 3
Budget: ₹1,500 – ₹12,500 INR
I’m building a single mobile-first experience that helps users discover the perfect film, jump straight into their chosen streaming service, and instantly share eye-catching AI-generated posters with friends.
Here’s what I need:
• An AI-driven recommendation engine that focuses first on Movies. It should learn from viewing history, ratings, and basic profile traits to surface personalised suggestions.
• Direct aggregation of catalogue metadata from Netflix, Amazon Prime and Disney+. Whenever a user taps “Watch”, the app must deep-link them to the exact title on the corresponding OTT platform.
• An image generation pipeline (Stable Diffusion, Midjourney or similar—your call) that produces stylised social-media-ready posters for each recommended movie, complete with title, tagline and a QR code back to the app.
• Clean, well-documented APIs so my front-end team can plug everything in without friction.
Acceptance criteria will include recommendation precision on a small test set, successful deep-links for all three OTTs, and poster generation in under ten seconds per request.
When you apply, please send a detailed project proposal outlining your chosen tech stack, data acquisition strategy for the three platforms, and how you’ll train or fine-tune the recommendation model. Links to past work are welcome, but I’m primarily interested in the plan you’d execute for this build.
Here’s what I need:
• An AI-driven recommendation engine that focuses first on Movies. It should learn from viewing history, ratings, and basic profile traits to surface personalised suggestions.
• Direct aggregation of catalogue metadata from Netflix, Amazon Prime and Disney+. Whenever a user taps “Watch”, the app must deep-link them to the exact title on the corresponding OTT platform.
• An image generation pipeline (Stable Diffusion, Midjourney or similar—your call) that produces stylised social-media-ready posters for each recommended movie, complete with title, tagline and a QR code back to the app.
• Clean, well-documented APIs so my front-end team can plug everything in without friction.
Acceptance criteria will include recommendation precision on a small test set, successful deep-links for all three OTTs, and poster generation in under ten seconds per request.
When you apply, please send a detailed project proposal outlining your chosen tech stack, data acquisition strategy for the three platforms, and how you’ll train or fine-tune the recommendation model. Links to past work are welcome, but I’m primarily interested in the plan you’d execute for this build.