{"title":"Featurerank:一种具有聚类和提升的非线性列表方法","authors":"Yongqing Wang, W. Mao","doi":"10.1109/YCICT.2010.5713050","DOIUrl":null,"url":null,"abstract":"Listwise is an important approach in learning to rank. Most of the existing lisewise methods use a linear ranking function which can only achieve a limited performance being applied to complex ranking problem. This paper proposes a non-linear listwise algorithm inspired by boosting and clustering. Different from the previous listwise approaches, our algorithm constructs weak rankers through directly discovering hidden order in single feature, and then combines these weak rankers using a boosting procedure. To discover the hidden order, we utilize (KNN) method. In our preliminary experiment, we compare our approach with other listwise algorithms and show the effectiveness of our proposed algorithm.","PeriodicalId":179847,"journal":{"name":"2010 IEEE Youth Conference on Information, Computing and Telecommunications","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Featurerank: A non-linear listwise approach with clustering and boosting\",\"authors\":\"Yongqing Wang, W. Mao\",\"doi\":\"10.1109/YCICT.2010.5713050\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Listwise is an important approach in learning to rank. Most of the existing lisewise methods use a linear ranking function which can only achieve a limited performance being applied to complex ranking problem. This paper proposes a non-linear listwise algorithm inspired by boosting and clustering. Different from the previous listwise approaches, our algorithm constructs weak rankers through directly discovering hidden order in single feature, and then combines these weak rankers using a boosting procedure. To discover the hidden order, we utilize (KNN) method. In our preliminary experiment, we compare our approach with other listwise algorithms and show the effectiveness of our proposed algorithm.\",\"PeriodicalId\":179847,\"journal\":{\"name\":\"2010 IEEE Youth Conference on Information, Computing and Telecommunications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE Youth Conference on Information, Computing and Telecommunications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/YCICT.2010.5713050\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE Youth Conference on Information, Computing and Telecommunications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/YCICT.2010.5713050","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Featurerank: A non-linear listwise approach with clustering and boosting
Listwise is an important approach in learning to rank. Most of the existing lisewise methods use a linear ranking function which can only achieve a limited performance being applied to complex ranking problem. This paper proposes a non-linear listwise algorithm inspired by boosting and clustering. Different from the previous listwise approaches, our algorithm constructs weak rankers through directly discovering hidden order in single feature, and then combines these weak rankers using a boosting procedure. To discover the hidden order, we utilize (KNN) method. In our preliminary experiment, we compare our approach with other listwise algorithms and show the effectiveness of our proposed algorithm.