{"title":"A fast and accurate collaborative filter","authors":"W. Deng, Qinghua Zheng, Lin Chen","doi":"10.1109/GRC.2009.5255149","DOIUrl":null,"url":null,"abstract":"There are two key issues for collaborative filtering: curse of dimension and long-consuming training. In our proposed algorithm, the curse of dimension problem is resolved by the proposed Reduced-SVD technique effectively and long-consuming training is addressed by Extreme Learning Machine (ELM) which is hundreds of times faster than iterative algorithms (e.g. BP). This will enable the algorithm more accurate and faster.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"19 4","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Conference on Granular Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GRC.2009.5255149","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
There are two key issues for collaborative filtering: curse of dimension and long-consuming training. In our proposed algorithm, the curse of dimension problem is resolved by the proposed Reduced-SVD technique effectively and long-consuming training is addressed by Extreme Learning Machine (ELM) which is hundreds of times faster than iterative algorithms (e.g. BP). This will enable the algorithm more accurate and faster.