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.

本研究探究回推协作过滤算法中被反馈循环所影响的用户评分矩阵，提出了衡量推荐系统对其整个评分矩阵影响的度量，并通过合成和实际数据集验证了其在还原固有用户偏好上的可用性。