{"title":"Discovering and Modelling Multiple Interests of Users in Collaborative Tagging Systems","authors":"C. Yeung, Nicholas Gibbins, N. Shadbolt","doi":"10.1109/WIIAT.2008.267","DOIUrl":null,"url":null,"abstract":"We analyse data obtained from several collaborative tagging systems and discover that user interests can be very diverse. Traditional methods for representing interests of users are usually not able to reflect such diversity. We propose a method to construct user profiles of multiple interests using data in a collaborative tagging system. Our evaluation suggests that the proposed method is able to generate user profiles which reflect the diversity of user interests and can be used to help provide more focused recommendation.","PeriodicalId":393772,"journal":{"name":"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2008-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WIIAT.2008.267","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 17
Abstract
We analyse data obtained from several collaborative tagging systems and discover that user interests can be very diverse. Traditional methods for representing interests of users are usually not able to reflect such diversity. We propose a method to construct user profiles of multiple interests using data in a collaborative tagging system. Our evaluation suggests that the proposed method is able to generate user profiles which reflect the diversity of user interests and can be used to help provide more focused recommendation.