{"title":"应用程序的整体排名方案","authors":"N. Chowdhury, R. Raje","doi":"10.1109/ICCITECHN.2018.8631955","DOIUrl":null,"url":null,"abstract":"App stores or application distribution platforms allow users to present their sentiments about apps in the forms of ratings and reviews. However, selecting the “best one” from available apps that offer similar functionality is difficult task - especially, if the selection process only uses the average star rating of the apps. To address this challenge, we have introduced a trust-based selection and ranking system of similar apps by combining the programmatic view (“internal view”) and the sentiments based on users reviews (“external view”). The rankings based on the average star ratings are compared with the rankings generated by our approach. We empirically evaluate our approach by using the publically available apps from the Google Play Store. For this study, we have chosen a dataset of 250 apps with total 114,480 reviews from top 5 different categories - of which we focused our experiments on 90 apps that have at least 1000 reviews. Our experiments indicate that proposed holistic ranking that encompasses both the internal and external views is a better alternative than any ranking that focuses only on the internal or external view.","PeriodicalId":355984,"journal":{"name":"2018 21st International Conference of Computer and Information Technology (ICCIT)","volume":"423 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A Holistic Ranking Scheme for Apps\",\"authors\":\"N. Chowdhury, R. Raje\",\"doi\":\"10.1109/ICCITECHN.2018.8631955\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"App stores or application distribution platforms allow users to present their sentiments about apps in the forms of ratings and reviews. However, selecting the “best one” from available apps that offer similar functionality is difficult task - especially, if the selection process only uses the average star rating of the apps. To address this challenge, we have introduced a trust-based selection and ranking system of similar apps by combining the programmatic view (“internal view”) and the sentiments based on users reviews (“external view”). The rankings based on the average star ratings are compared with the rankings generated by our approach. We empirically evaluate our approach by using the publically available apps from the Google Play Store. For this study, we have chosen a dataset of 250 apps with total 114,480 reviews from top 5 different categories - of which we focused our experiments on 90 apps that have at least 1000 reviews. Our experiments indicate that proposed holistic ranking that encompasses both the internal and external views is a better alternative than any ranking that focuses only on the internal or external view.\",\"PeriodicalId\":355984,\"journal\":{\"name\":\"2018 21st International Conference of Computer and Information Technology (ICCIT)\",\"volume\":\"423 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 21st International Conference of Computer and Information Technology (ICCIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCITECHN.2018.8631955\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 21st International Conference of Computer and Information Technology (ICCIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCITECHN.2018.8631955","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
摘要
应用商店或应用分发平台允许用户以评分和评论的形式表达他们对应用的看法。然而,从提供类似功能的应用程序中选择“最佳应用”是一项艰巨的任务,尤其是在选择过程中只使用应用程序的平均星级评分的情况下。为了应对这一挑战,我们引入了一个基于信任的选择和排名系统,结合了程序化视图(“内部视图”)和基于用户评论的情绪(“外部视图”)。将基于平均星级评级的排名与我们的方法生成的排名进行比较。我们通过使用Google Play Store中的公开应用来评估我们的方法。在这项研究中,我们从前5个不同类别中选择了250个应用程序的数据集,总共有114,480条评论,其中我们将实验重点放在90个至少有1000条评论的应用程序上。我们的实验表明,提议的整体排名包括内部和外部的观点是一个更好的选择,而不是任何排名只关注内部或外部的观点。
App stores or application distribution platforms allow users to present their sentiments about apps in the forms of ratings and reviews. However, selecting the “best one” from available apps that offer similar functionality is difficult task - especially, if the selection process only uses the average star rating of the apps. To address this challenge, we have introduced a trust-based selection and ranking system of similar apps by combining the programmatic view (“internal view”) and the sentiments based on users reviews (“external view”). The rankings based on the average star ratings are compared with the rankings generated by our approach. We empirically evaluate our approach by using the publically available apps from the Google Play Store. For this study, we have chosen a dataset of 250 apps with total 114,480 reviews from top 5 different categories - of which we focused our experiments on 90 apps that have at least 1000 reviews. Our experiments indicate that proposed holistic ranking that encompasses both the internal and external views is a better alternative than any ranking that focuses only on the internal or external view.