{"title":"基于模糊关系信誉模型的个性化推荐算法","authors":"Meiyu Fang, Xiaolin Zheng, Deren Chen","doi":"10.1109/IJCSS.2011.45","DOIUrl":null,"url":null,"abstract":"For overcoming the problems that the traditional collaborative filtering personalized recommender algorithm which called KNN has such as cold-starting, data sparsity, flexibility and black box, a new personalized recommender algorithm based on fuzzy reputation model(called FRPRA) is proposed. We analyze the steps of FRPRA and the differences between it and KNN, explore the ways how FRPRA overcomes the existed problems of KNN. At the same time, we compare the performance of this two algorithms.","PeriodicalId":251415,"journal":{"name":"2011 International Joint Conference on Service Sciences","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Personalized Recommender Algorithm Based on Fuzzy Relation Reputation Model\",\"authors\":\"Meiyu Fang, Xiaolin Zheng, Deren Chen\",\"doi\":\"10.1109/IJCSS.2011.45\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For overcoming the problems that the traditional collaborative filtering personalized recommender algorithm which called KNN has such as cold-starting, data sparsity, flexibility and black box, a new personalized recommender algorithm based on fuzzy reputation model(called FRPRA) is proposed. We analyze the steps of FRPRA and the differences between it and KNN, explore the ways how FRPRA overcomes the existed problems of KNN. At the same time, we compare the performance of this two algorithms.\",\"PeriodicalId\":251415,\"journal\":{\"name\":\"2011 International Joint Conference on Service Sciences\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-05-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 International Joint Conference on Service Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCSS.2011.45\",\"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 International Joint Conference on Service Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCSS.2011.45","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Personalized Recommender Algorithm Based on Fuzzy Relation Reputation Model
For overcoming the problems that the traditional collaborative filtering personalized recommender algorithm which called KNN has such as cold-starting, data sparsity, flexibility and black box, a new personalized recommender algorithm based on fuzzy reputation model(called FRPRA) is proposed. We analyze the steps of FRPRA and the differences between it and KNN, explore the ways how FRPRA overcomes the existed problems of KNN. At the same time, we compare the performance of this two algorithms.