{"title":"DiRec:无语义协同过滤的多样化建议","authors":"Rubi Boim, T. Milo, Slava Novgorodov","doi":"10.1109/ICDE.2011.5767942","DOIUrl":null,"url":null,"abstract":"In this demo we present DiRec, a plug-in that allows Collaborative Filtering (CF) Recommender systems to diversify the recommendations that they present to users. DiRec estimates items diversity by comparing the rankings that different users gave to the items, thereby enabling diversification even in common scenarios where no semantic information on the items is available. Items are clustered based on a novel notion of priority-medoids that provides a natural balance between the need to present highly ranked items vs. highly diverse ones. We demonstrate the operation of DiRec in the context of a movie recommendation system. We show the advantage of recommendation diversification and its feasibility even in the absence of semantic information.","PeriodicalId":332374,"journal":{"name":"2011 IEEE 27th International Conference on Data Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"DiRec: Diversified recommendations for semantic-less Collaborative Filtering\",\"authors\":\"Rubi Boim, T. Milo, Slava Novgorodov\",\"doi\":\"10.1109/ICDE.2011.5767942\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this demo we present DiRec, a plug-in that allows Collaborative Filtering (CF) Recommender systems to diversify the recommendations that they present to users. DiRec estimates items diversity by comparing the rankings that different users gave to the items, thereby enabling diversification even in common scenarios where no semantic information on the items is available. Items are clustered based on a novel notion of priority-medoids that provides a natural balance between the need to present highly ranked items vs. highly diverse ones. We demonstrate the operation of DiRec in the context of a movie recommendation system. We show the advantage of recommendation diversification and its feasibility even in the absence of semantic information.\",\"PeriodicalId\":332374,\"journal\":{\"name\":\"2011 IEEE 27th International Conference on Data Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-04-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE 27th International Conference on Data Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDE.2011.5767942\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE 27th International Conference on Data Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDE.2011.5767942","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
DiRec: Diversified recommendations for semantic-less Collaborative Filtering
In this demo we present DiRec, a plug-in that allows Collaborative Filtering (CF) Recommender systems to diversify the recommendations that they present to users. DiRec estimates items diversity by comparing the rankings that different users gave to the items, thereby enabling diversification even in common scenarios where no semantic information on the items is available. Items are clustered based on a novel notion of priority-medoids that provides a natural balance between the need to present highly ranked items vs. highly diverse ones. We demonstrate the operation of DiRec in the context of a movie recommendation system. We show the advantage of recommendation diversification and its feasibility even in the absence of semantic information.