  Collaborative filtering is a popular technique to infer users' preferences on
new content based on the collective information of all users preferences.
Recommender systems then use this information to make personalized suggestions
to users. When users accept these recommendations it creates a feedback loop in
the recommender system, and these loops iteratively influence the collaborative
filtering algorithm's predictions over time. We investigate whether it is
possible to identify items affected by these feedback loops. We state
sufficient assumptions to deconvolve the feedback loops while keeping the
inverse solution tractable. We furthermore develop a metric to unravel the
recommender system's influence on the entire user-item rating matrix. We use
this metric on synthetic and real-world datasets to (1) identify the extent to
which the recommender system affects the final rating matrix, (2) rank
frequently recommended items, and (3) distinguish whether a user's rated item
was recommended or an intrinsic preference. Our results indicate that it is
possible to recover the ratings matrix of intrinsic user preferences using a
single snapshot of the ratings matrix without any temporal information.
