Ranking of news items in rule-stringent social media based on users' importance: A social computing approach

K. Ntalianis, Abdel-badeeh M. Salem
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引用次数: 2

Abstract

In this paper an innovative social media news items ranking scheme is proposed. The proposed unsupervised architecture takes into consideration user-content interactions, since social media posts receive likes, comments and shares from friends and other users. Additionally the importance of each user is modeled, based on an innovative algorithm that borrows ideas from the PageRank algorithm. Finally, a novel content ranking component is introduced, which ranks posted news items based on a social computing method, driven by the importance of the social network users that interact with them. Initial experiments on real life social networks news items illustrate the promising performance of the proposed architecture. Additionally comparisons with three different ranking ways are provided (SUMF, RSN-CO and RSN-nCO), in terms of user satisfaction.
基于用户重要性的严格规则的社交媒体新闻项目排名:一种社会计算方法
本文提出了一种创新的社交媒体新闻排序方案。提议的无监督架构考虑了用户与内容的交互,因为社交媒体帖子会收到朋友和其他用户的喜欢、评论和分享。此外,基于借鉴PageRank算法的创新算法,对每个用户的重要性进行建模。最后,介绍了一种新颖的内容排名组件,该组件基于社交计算方法,根据与之交互的社交网络用户的重要性对发布的新闻进行排名。对现实生活中的社交网络新闻项目的初步实验表明,所提出的体系结构具有良好的性能。此外,还比较了三种不同的排名方式(SUMF、RSN-CO和RSN-nCO)在用户满意度方面的差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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