{"title":"移动通知数据集的综合与评价","authors":"Kieran Fraser, Bilal Yousuf, Owen Conlan","doi":"10.1145/3099023.3099046","DOIUrl":null,"url":null,"abstract":"Open-source mobile notification datasets are a rarity in the research community. Due to the sensitive nature of mobile notifications it is difficult to find a dataset which captures their features in such a way that their inherently personal information is kept private. For this reason, the majority of research in the domain of Notification Management requires ad-hoc software to be developed for capturing the data necessary to test hypotheses, train algorithms and evaluate proposed systems. As an alternative, this paper discusses the process, advantages and limitations with harnessing a large-scale mobile usage dataset for deriving a synthetic mobile notification dataset used in testing and improving an intelligent Notification Management System (NMS).","PeriodicalId":219391,"journal":{"name":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","volume":"59 8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Synthesis & Evaluation of a Mobile Notification Dataset\",\"authors\":\"Kieran Fraser, Bilal Yousuf, Owen Conlan\",\"doi\":\"10.1145/3099023.3099046\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Open-source mobile notification datasets are a rarity in the research community. Due to the sensitive nature of mobile notifications it is difficult to find a dataset which captures their features in such a way that their inherently personal information is kept private. For this reason, the majority of research in the domain of Notification Management requires ad-hoc software to be developed for capturing the data necessary to test hypotheses, train algorithms and evaluate proposed systems. As an alternative, this paper discusses the process, advantages and limitations with harnessing a large-scale mobile usage dataset for deriving a synthetic mobile notification dataset used in testing and improving an intelligent Notification Management System (NMS).\",\"PeriodicalId\":219391,\"journal\":{\"name\":\"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization\",\"volume\":\"59 8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3099023.3099046\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3099023.3099046","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Synthesis & Evaluation of a Mobile Notification Dataset
Open-source mobile notification datasets are a rarity in the research community. Due to the sensitive nature of mobile notifications it is difficult to find a dataset which captures their features in such a way that their inherently personal information is kept private. For this reason, the majority of research in the domain of Notification Management requires ad-hoc software to be developed for capturing the data necessary to test hypotheses, train algorithms and evaluate proposed systems. As an alternative, this paper discusses the process, advantages and limitations with harnessing a large-scale mobile usage dataset for deriving a synthetic mobile notification dataset used in testing and improving an intelligent Notification Management System (NMS).