{"title":"基于元数据的有效协同推荐组合方法","authors":"K. Kim, Jun Yeop Lee, Y. Choi","doi":"10.1145/2663761.2664189","DOIUrl":null,"url":null,"abstract":"In this paper, we propose content-metadata based combined approach to effective collaborative recommendation. Our approach combines user-item rating scores and/or trust network information with content-metadata compensatively for boosting collaborative recommendation. In experiment, we identified that our approach could considerably improve recommendation performance when compared to existing collaborative recommendation methods.","PeriodicalId":120340,"journal":{"name":"Research in Adaptive and Convergent Systems","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Metadata based combined approach for effective collaborative recommendation\",\"authors\":\"K. Kim, Jun Yeop Lee, Y. Choi\",\"doi\":\"10.1145/2663761.2664189\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose content-metadata based combined approach to effective collaborative recommendation. Our approach combines user-item rating scores and/or trust network information with content-metadata compensatively for boosting collaborative recommendation. In experiment, we identified that our approach could considerably improve recommendation performance when compared to existing collaborative recommendation methods.\",\"PeriodicalId\":120340,\"journal\":{\"name\":\"Research in Adaptive and Convergent Systems\",\"volume\":\"48 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-10-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Research in Adaptive and Convergent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2663761.2664189\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research in Adaptive and Convergent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2663761.2664189","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Metadata based combined approach for effective collaborative recommendation
In this paper, we propose content-metadata based combined approach to effective collaborative recommendation. Our approach combines user-item rating scores and/or trust network information with content-metadata compensatively for boosting collaborative recommendation. In experiment, we identified that our approach could considerably improve recommendation performance when compared to existing collaborative recommendation methods.