  Social media and online navigation bring us enjoyable experience in accessing
information, and simultaneously create information cocoons (ICs) in which we
are unconsciously trapped with limited and biased information. We provide a
formal definition of IC in the scenario of online navigation. Subsequently, by
analyzing real recommendation networks extracted from Science, PNAS and Amazon
websites, and testing mainstream algorithms in disparate recommender systems,
we demonstrate that similarity-based recommendation techniques result in ICs,
which suppress the system navigability by hundreds of times. We further propose
a flexible recommendation strategy that solves the IC-induced problem and
improves retrieval accuracy in navigation, demonstrated by simulations on real
data and online experiments on the largest video website in China.
