Appreciate that point that they are competing for time (as I was only thinking of monopolies over content).
If the reason it isn’t used is that users don’t “trust that the system will give what they want given a single short description”, then part of the research agenda for aligned recommender systems is not just producing systems that are aligned, but systems where their users have a greater degree of justified trust that they are aligned (placing more emphasis on the user’s experience of interacting with the system). Some of this research could potentially take place with existing classification-based filters.
One tool that I think would be quite useful is having some kind of website where you gather:
Situations: descriptions of decisions that people are facing, and their options
Outcomes: the option that they took, and how they felt about it after the fact
Then you could get a description of a decision that someone new is facing and automatically assemble a reference class for them of people with the most similar decisions and how they turned out. Could work without any ML, but language modelling to cluster similar situations would help.
Kind of similar information to a review site, but hopefully can aggregate by situation instead of by product used, and cover decisions that are not in the category of “pick a product to buy”