{"title":"用用户反馈评估网络事件的影响","authors":"Shobha Venkataraman, Jia Wang","doi":"10.1145/3229543.3229553","DOIUrl":null,"url":null,"abstract":"User feedback data, generated when users call customer care agents with problems, is a valuable source of data for understanding network problems from users' perspectives. However, this data is extremely noisy. In this paper, we design a framework, LOTUS, to assess the user impact of network events from the user feedback, through a novel algorithmic composition of co-training and spatial scan statistics. Through experimental analysis on synthetic and real data, we show the accuracy and practical nature of LOTUS.","PeriodicalId":198478,"journal":{"name":"Proceedings of the 2018 Workshop on Network Meets AI & ML","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Assessing the Impact of Network Events with User Feedback\",\"authors\":\"Shobha Venkataraman, Jia Wang\",\"doi\":\"10.1145/3229543.3229553\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"User feedback data, generated when users call customer care agents with problems, is a valuable source of data for understanding network problems from users' perspectives. However, this data is extremely noisy. In this paper, we design a framework, LOTUS, to assess the user impact of network events from the user feedback, through a novel algorithmic composition of co-training and spatial scan statistics. Through experimental analysis on synthetic and real data, we show the accuracy and practical nature of LOTUS.\",\"PeriodicalId\":198478,\"journal\":{\"name\":\"Proceedings of the 2018 Workshop on Network Meets AI & ML\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2018 Workshop on Network Meets AI & ML\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3229543.3229553\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2018 Workshop on Network Meets AI & ML","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3229543.3229553","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Assessing the Impact of Network Events with User Feedback
User feedback data, generated when users call customer care agents with problems, is a valuable source of data for understanding network problems from users' perspectives. However, this data is extremely noisy. In this paper, we design a framework, LOTUS, to assess the user impact of network events from the user feedback, through a novel algorithmic composition of co-training and spatial scan statistics. Through experimental analysis on synthetic and real data, we show the accuracy and practical nature of LOTUS.
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