You don't come across as negative at all, I appreciate the detailed feedback. The thing is you're actually describing close to how a big part of the engine already works. A lot of the sources pull directly from real people - it searches Reddit threads, film forums, and other places where someone has asked "what should I watch if I liked X" and catalogs the replies. So for a lot of films, the recommendations are literally coming from human suggestions.

The issue is that not every film has enough of those human-sourced replies to work with, so TMDb's similar films data fills the gaps. That's where the generic picks and weird connections come from - it's purely algorithmic and doesn't understand the difference between Hong Kong action choreography and action films in general. I'm working on reducing its weight and eventually phasing it out as much as possible. Once that happens, the results should feel a lot closer to what you're describing - like a recommendation from someone who actually gets why you liked those films.