Application of Liquid Rank Reputation System for Content Recommendation
This is an incremental improvement for social media platforms aiming to enhance recommendation accuracy and diversity by incorporating higher-order social network connections.
The paper tackles the problem of content recommendation on social media by proposing a liquid democracy-based model using a reputation ranking system to personalize recommendations, analyzing a Twitter dataset on cryptocurrency news to identify opinion leaders.
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.