This post describes work being presented at WWW 2014, by Tien Nguyen
Those of you following recommender systems have almost certainly heard the debate about filter bubbles. This concept, perhaps best articulated by Eli Pariser, argues that recommenders have the potential to trap users into increasingly similar content, isolating them from the diversity of content that makes people rich learners.
We decided to test this concept empirically, using longitudinal data from MovieLens. Specifically, we wanted to answer two questions:
Do recommended movies get narrower as users continue to rate movies?
Do users consume narrower movies — and if so, is this a consequence of taking recommendations?
What we found surprised us.