An effective content recommendation on social media platforms should be able to benefit both creators to earn fair compensation and consumers to enjoy really relevant, interesting, and personalized content. In this paper, we propose a model to implement the liquid democracy principle for the content recommendation system. It uses a personalized recommendation model based on reputation ranking system to encourage personal interests driven recommendation. Moreover, the personalization factors to an end users' higher-order friends on the social network (initial input Twitter channels in our case study) to improve the accuracy and diversity of recommendation results. This paper analyzes the dataset based on cryptocurrency news on Twitter to find the opinion leader using the liquid rank reputation system. This paper deals with the tier-2 implementation of a liquid rank in a content recommendation model. This model can be also used as an additional layer in the other recommendation systems. The paper proposes the implementation, challenges, and future scope of the liquid rank reputation model.

翻译：有关社交媒体平台的有效内容建议应能使创作者受益,以获得公平的补偿,使消费者享有真正相关、有趣和个性化的内容。在本文中,我们提出了一个执行内容建议系统流动民主原则的模式。它使用基于名声评级制度的个性化建议模式,鼓励个人利益驱动建议。此外,社会网络最终用户较高级朋友的个人化因素(我们案例研究中的初始输入推特频道),以提高建议结果的准确性和多样性。本文分析了基于Twitter加密货币新闻的数据集,以便利用液态名声系统找到舆论领袖。本文涉及在内容建议模式中实施2级液态名次的问题。这一模式也可以作为其他建议系统的额外层面使用。本文件提出了液体名声模型的实施、挑战和未来范围。