  We explore a hidden feedback loops effect in online recommender systems.
Feedback loops result in degradation of online multi-armed bandit (MAB)
recommendations to a small subset and loss of coverage and novelty. We study
how uncertainty and noise in user interests influence the existence of feedback
loops. First, we show that an unbiased additive random noise in user interests
does not prevent a feedback loop. Second, we demonstrate that a non-zero
probability of resetting user interests is sufficient to limit the feedback
loop and estimate the size of the effect. Our experiments confirm the
theoretical findings in a simulated environment for four bandit algorithms.
