对社交媒体上的新闻动态更新进行排名:一种审查和专业知识意识方法

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
S. Belkacem, K. Boukhalfa
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引用次数: 0

摘要

全球有数亿用户使用社交媒体。在这些平台上,任何用户都可以在他的社交网络上发布和分享更新。由于大量的数据,用户被按时间顺序显示的更新淹没了。此外,其中大多数都是无关紧要的。根据相关性对新闻源更新进行排序,以帮助用户快速赶上相关更新。在这项工作中,作者首先根据四个主要标准研究了该领域提出的方法:可能影响相关性的特征、相关性预测模型、训练和评估方法以及评估平台。然后作者提出了一种方法,该方法利用了另一种类型的特性,即更新作者对相应主题的专业知识。Twitter上的实验结果强调,评判专业知识对于最大限度地提高新闻源更新的相关性至关重要,而这一点在学术界和工业界都没有得到考虑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ranking News Feed Updates on Social Media: A Review and Expertise-Aware Approach
Social media are used by hundreds of millions of users worldwide. On these platforms, any user can post and share updates with individuals from his social network. Due to the large amount of data, users are overwhelmed by updates displayed chronologically in their newsfeed. Moreover, most of them are irrelevant. Ranking newsfeed updates in order of relevance is proposed to help users quickly catch up with the relevant updates. In this work, the authors first study approaches proposed in this area according to four main criteria: features that may influence relevance, relevance prediction models, training and evaluation methods, and evaluation platforms. Then the authors propose an approach that leverages another type of feature which is the expertise of the update's author for the corresponding topics. Experimental results on Twitter highlight that judging expertise, which has not been considered in the academic and the industrial communities, is crucial for maximizing the relevance of updates in newsfeeds.
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来源期刊
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
0.00%
发文量
20
审稿时长
>12 weeks
期刊介绍: The International Journal of Data Warehousing and Mining (IJDWM) disseminates the latest international research findings in the areas of data management and analyzation. IJDWM provides a forum for state-of-the-art developments and research, as well as current innovative activities focusing on the integration between the fields of data warehousing and data mining. Emphasizing applicability to real world problems, this journal meets the needs of both academic researchers and practicing IT professionals.The journal is devoted to the publications of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. The journal accepts paper submission of any work relevant to data warehousing and data mining. Special attention will be given to papers focusing on mining of data from data warehouses; integration of databases, data warehousing, and data mining; and holistic approaches to mining and archiving
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