Abstract/Summary

Social media and online navigation bring us enjoyable experiences 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 analysing 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 addresses the IC-induced problem and improves retrieval accuracy in navigation, which are demonstrated by simulations on real data and online experiments on the largest video website in China. Aligned with legal acts and regulations in many countries on the transparency of algorithms and users' rights, this paper describes a typical and significant challenge in a quantitative manner and presents a viable solution, which will make a profound impact on the industrial design of algorithms, future scientific studies, as well as policy making.