Discovering non-interesting attributes I would like to ask a question about recommender systems. We are showing some movies to users and they have to decide if they like them or not. These movies have only a few attributes

Title
Director
Category
Duration
Nationality

We show the attributes as a block. The users can decide if they like (the block) or not. We aggregate these decisions to recommend them more movies in the future. My question is: Are there any methods that can tell us whether a user is uninterested in a given attribute? For example, if a user is never considering duration when saying yes or no.
As a simple measure, you could simply take the point biserial correlation between duration and rating for a given user.  If they aren't correlated (positively or negatively), this variable is likely not important to that user.

Another way to determine the impact of a variable is by training another model excluding that variable and determining how that affects the quality of the predictions.