Application of Liquid Rank Reputation System for Content Recommendation

TL;DR

This paper introduces a liquid rank reputation system integrated into a personalized content recommendation model for social media, enhancing relevance and diversity by leveraging social network influence and opinion leaders.

Contribution

It presents a novel implementation of the liquid democracy principle in content recommendation, incorporating reputation ranking and higher-order social influence to improve personalization.

Findings

Identified opinion leaders using liquid rank on Twitter cryptocurrency news

Enhanced recommendation accuracy and diversity through social influence modeling

Proposed a scalable, layered recommendation system with future scope

Abstract

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.