Sara Bouzit, Gaëlle Calvary, D. Chêne, J. Vanderdonckt
{"title":"Interface adaptivity by widget promotion/demotion","authors":"Sara Bouzit, Gaëlle Calvary, D. Chêne, J. Vanderdonckt","doi":"10.1145/3319499.3328237","DOIUrl":null,"url":null,"abstract":"Promotion and demotion are a typical adaptive navigation technique making a page or a link easier to select by emphasizing it or de-emphasizing it depending on its popularity. This technique, which was successfully applied to adaptive web sites, is now generalized to mainstream graphical user interfaces by introducing bimotion user interfaces, which constantly and dynamically perform adaptivity by promoting the most predicted widgets and demoting the least predicted ones either in context or in a separated prediction window. Promoted widgets that are less frequently used become demoted, demoted widgets that are more frequently used become promoted.","PeriodicalId":185267,"journal":{"name":"Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems","volume":"780 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3319499.3328237","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Promotion and demotion are a typical adaptive navigation technique making a page or a link easier to select by emphasizing it or de-emphasizing it depending on its popularity. This technique, which was successfully applied to adaptive web sites, is now generalized to mainstream graphical user interfaces by introducing bimotion user interfaces, which constantly and dynamically perform adaptivity by promoting the most predicted widgets and demoting the least predicted ones either in context or in a separated prediction window. Promoted widgets that are less frequently used become demoted, demoted widgets that are more frequently used become promoted.