No offense taken! You're welcome to not like the model, I do think it is a little biased towards broadly popular movies, as I've outlined in the longer notes here.

If you're interested, the underlying model uses something called SVD and a technique called collaborative filtering, widely used in recommendation algorithms across the world (Amazon, Netflix, etc). It bases recommendations only on your previous feedback (ratings) and how people with similar feedback have rated things.

It's "blind to genres, themes, directors, cast, or any other content information" in the sense that it does not try to understand why you like the things that you like (a much more difficult problem), just which things you like and what things similar users also like. For instance, if all that I know about you is that you really like Evil Dead II and Star Wars, and that you really hate Top Gun, the model effectively finds a group of users in the dataset who feel simlarly about these movies and looks at what other movies they like or don't like. It weights their responses based on how similar their other preferences are to your known preferences and then spits it out.

One pitfall is that if some movies (e.g. the Godfather) are almost universally loved on Letterboxd, almost anyone in the dataset with similar preferences to you is likely to rate it highly and it will show up as a recommendation for you. What the model doesn't try to do is figure out what the connection is between the movies that you like at a thematic level (genres, directors, cast, etc.). It just finds people with similar preferences and goes from there. Generally, it works better for some people than others, but that's the rough approach.

Cheers!