{"title":"Personalized Tweet Ranking Based on AHP: A Case Study of Micro-blogging Message Ranking in T.Sina","authors":"Yuhong Guo, Li-Fang Kang, Tie Shi","doi":"10.1109/WI-IAT.2012.38","DOIUrl":null,"url":null,"abstract":"Micro-blog's handiness is besieging users with overloaded short snippets of tweets surging into their page. How to evaluate quality of tweets with limited content and rank them to direct user attention is a new significant topic. In this paper, we study the problem of user-specific tweet evaluation and ranking. We propose a comprehensive, personalized tweet ranking mechanism (Tweet Rank) with a technique of AHP (Analytic Hierarchy Process) in operational research. Based on mathematics and psychology, the AHP can quantify the weight of each impact factor and model user blur preference precisely. Case study in Chinese micro-blog platform of T.sina showed that Tweet Rank greatly outperformed time-based ranking used in T.Sina, improving percentage of interesting content in Top10 to 60% from 20%.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WI-IAT.2012.38","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
Micro-blog's handiness is besieging users with overloaded short snippets of tweets surging into their page. How to evaluate quality of tweets with limited content and rank them to direct user attention is a new significant topic. In this paper, we study the problem of user-specific tweet evaluation and ranking. We propose a comprehensive, personalized tweet ranking mechanism (Tweet Rank) with a technique of AHP (Analytic Hierarchy Process) in operational research. Based on mathematics and psychology, the AHP can quantify the weight of each impact factor and model user blur preference precisely. Case study in Chinese micro-blog platform of T.sina showed that Tweet Rank greatly outperformed time-based ranking used in T.Sina, improving percentage of interesting content in Top10 to 60% from 20%.
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