This post is in Estzter territory, and probably just reflects ignorance on my part, but I’d be grateful for the information from those in the know, anyway. Following “one of Eszter’s posts recently”:https://crookedtimber.org/2005/02/07/networks-and-tastes/ , I signed up to “Movielens”:http://movielens.umn.edu/ and have been dutifully entering my ratings in various spare moments. Like Amazon, “Movielens”:http://movielens.umn.edu/ tells me that based on the movies I like I should check out various other ones. Presumably, the program checks the database to see which movies I haven’t seen are highly rated by other people who like the same films that I liked (ditto Amazon for books, dvds etc).
Now here’s my problem. When we all come to such systems “cold” (as it were), the links between our choices provide genuinely informative data. But once we start acting on the recommendations, even chance correlations can get magnified. So, for example, suppose we have three movies A, B and C. Perhaps if we showed these films to a randomly chosen audience there wouldn’t be any reason to suppose that people who like A prefer B to C or vice versa. But if the first N people to go to the expert system happen to like both A and B, then the program will spew out a recommendation to subsquent A or B lovers to follow up their viewing with B or A. And those people in turn, having viewed the recommended movie, will feed their approval back into the system and thereby strengthen the association. Poor old movie C, excluded by chance from this self-reinforcing loop, will not get recommended nearly so often.
I guess the people who design these systems must have considered these effects and how to counteract them. Any answers?