Collaborative filtering is a popular technique to infer usersâ€™ 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 algorithmâ€™s predictions over time. We investigate whether it is possible to identify items aected by these feedback loops. We state sucient assumptions to deconvolve the feedback loops while keeping the inverse solution tractable. We furthermore develop a metric to unravel the recommender systemâ€™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 aects the final rating matrix, (2) rank frequently recommended items, and (3) distinguish whether a userâ€™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.