Hou Lei, Pan Xue, Liu Kecheng, Yang Zimo, Liu Jianguo, Zhou Tao
School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Informatics Research Centre, University of Reading, Reading RG66UD, UK.
iScience. 2022 Dec 28;26(1):105893. doi: 10.1016/j.isci.2022.105893. eCollection 2023 Jan 20.
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 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 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. This paper quantifies the challenge of ICs in recommender systems and presents a viable solution, which offer insights into the industrial design of algorithms, future scientific studies, as well as policy making.
社交媒体和在线导航为我们获取信息带来了愉快的体验，但同时也制造了信息茧房，我们会不知不觉地被困在其中，接触到有限且有偏差的信息。我们给出了在线导航场景下信息茧房的正式定义。随后，通过分析从《科学》《美国国家科学院院刊》和亚马逊网站提取的真实推荐网络，并在不同的推荐系统中测试主流算法，我们证明基于相似性的推荐技术会导致信息茧房，使系统可导航性降低数百倍。我们进一步提出了一种灵活的推荐策略，该策略解决了由信息茧房引发的问题，并提高了导航中的检索准确性，这在对真实数据的模拟以及在中国最大的视频网站上进行的在线实验中得到了验证。本文量化了推荐系统中信息茧房的挑战，并提出了一个可行的解决方案，为算法的工业设计、未来的科学研究以及政策制定提供了见解。
