{"title":"无线推荐系统中学习与广播的交互作用","authors":"Linqi Song, C. Fragouli, D. Shah","doi":"10.1109/ISIT.2019.8849556","DOIUrl":null,"url":null,"abstract":"We consider recommendation systems that need to operate under wireless bandwidth constraints, which is measured as the number of broadcast transmissions. We demonstrate a (tight for some instances) tradeoff between regret and bandwidth for wireless recommendations formulated in a contextual multiarmed bandit framework.","PeriodicalId":6708,"journal":{"name":"2019 IEEE International Symposium on Information Theory (ISIT)","volume":"10 1","pages":"2549-2553"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Interactions Between Learning and Broadcasting in Wireless Recommendation Systems\",\"authors\":\"Linqi Song, C. Fragouli, D. Shah\",\"doi\":\"10.1109/ISIT.2019.8849556\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider recommendation systems that need to operate under wireless bandwidth constraints, which is measured as the number of broadcast transmissions. We demonstrate a (tight for some instances) tradeoff between regret and bandwidth for wireless recommendations formulated in a contextual multiarmed bandit framework.\",\"PeriodicalId\":6708,\"journal\":{\"name\":\"2019 IEEE International Symposium on Information Theory (ISIT)\",\"volume\":\"10 1\",\"pages\":\"2549-2553\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE International Symposium on Information Theory (ISIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISIT.2019.8849556\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Symposium on Information Theory (ISIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIT.2019.8849556","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Interactions Between Learning and Broadcasting in Wireless Recommendation Systems
We consider recommendation systems that need to operate under wireless bandwidth constraints, which is measured as the number of broadcast transmissions. We demonstrate a (tight for some instances) tradeoff between regret and bandwidth for wireless recommendations formulated in a contextual multiarmed bandit framework.