Personalized Film Catalog via AI Request

[Sophanos]

Stremio Version
Stremio Version and OS

Is your feature request related to a problem? Please describe.
Current recommendation systems often fail to provide highly specific or personalized suggestions, especially for niche or under-the-radar films. This makes it difficult for users to discover content that truly fits their preferences.

Describe the solution you'd like
Integrate an AI-powered chatbot (like GPT) into Stremio that allows users to generate tailored film catalogs based on detailed prompts. The AI would provide highly specific recommendations, creating a personalized catalog instantly based on the user's unique requests, making discovery more intuitive and customized.

Use Case Example:
"I’m looking for a bawdy, lighthearted, hilarious comedy like Super Troopers (2001), Grandma's Boy (2006), The Other Guys (2010), or Let It Ride (1989). Films that are funny, well-written, and possibly underrated. I’ve watched a lot, so I’m hoping for under-the-radar suggestions. Just list 30+ films that match."

How It Works:

User provides a specific request to the AI (like the example above).
AI generates a tailored list of film recommendations.
Stremio automatically creates a personalized catalog in the view based on these recommendations, allowing users to explore instantly.

Describe alternatives you've considered
Using existing recommendation engines like Trakt, but they lack the ability to handle detailed, specific requests.
Manually searching through different platforms and databases for films, which is time-consuming and often yields generic results.
Relying on user-generated lists or forums, but these rarely match niche preferences and aren’t dynamic.

Additional context
With AI-driven recommendations, users could effortlessly discover films based on unique, specific moods or criteria that are often hard to capture with traditional recommendation systems. For example, users can ask for films similar to a particular combination of genres, themes, or even emotional tones. This would not only improve the browsing experience but also allow users with extensive watch histories to find hidden gems or underrated films that typical algorithms miss.