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.

翻译：社交媒体和在线导航给我们带来获取信息的可喜经验,同时创造信息库(ICs),我们被无意识地困在信息有限和有偏差的信息中。我们在在线导航中提供了ICs的正式定义。随后,通过分析从科学、PNAS和亚马逊网站提取的真正建议网络,以及测试不同推荐人系统中的主流算法,我们证明基于相似性的建议技术导致ICs,使系统导航能力受挫数百倍。我们进一步提出灵活的建议战略,解决IC公司引发的问题,提高导航检索的准确性,在中国最大的视频网站上进行真实数据模拟和在线实验。