{"title":"基于最大相似团用户的协同过滤推荐算法","authors":"Zhaoyang Zhou, Y. He","doi":"10.1109/ISCC-C.2013.58","DOIUrl":null,"url":null,"abstract":"In order to improve the performance of Collaborative filtering (CF), a new method of producing the nearest neighbor for active user is proposed in this paper. Inspired by the conformist of E-commerce consumers, we build the user model of maximum similar clique and we use it to improve the method of producing the nearest neighbors for target users. A collaborative filtering recommendation algorithm MCQ-CF based on user model is present. The experiment results show that the algorithm MCQ-CF has good performance for accuracy and stability.","PeriodicalId":313511,"journal":{"name":"2013 International Conference on Information Science and Cloud Computing Companion","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2013-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Collaborative Filtering Recommendation Algorithm Based on Users of Maximum Similar Clique\",\"authors\":\"Zhaoyang Zhou, Y. He\",\"doi\":\"10.1109/ISCC-C.2013.58\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to improve the performance of Collaborative filtering (CF), a new method of producing the nearest neighbor for active user is proposed in this paper. Inspired by the conformist of E-commerce consumers, we build the user model of maximum similar clique and we use it to improve the method of producing the nearest neighbors for target users. A collaborative filtering recommendation algorithm MCQ-CF based on user model is present. The experiment results show that the algorithm MCQ-CF has good performance for accuracy and stability.\",\"PeriodicalId\":313511,\"journal\":{\"name\":\"2013 International Conference on Information Science and Cloud Computing Companion\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 International Conference on Information Science and Cloud Computing Companion\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCC-C.2013.58\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Information Science and Cloud Computing Companion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCC-C.2013.58","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Collaborative Filtering Recommendation Algorithm Based on Users of Maximum Similar Clique
In order to improve the performance of Collaborative filtering (CF), a new method of producing the nearest neighbor for active user is proposed in this paper. Inspired by the conformist of E-commerce consumers, we build the user model of maximum similar clique and we use it to improve the method of producing the nearest neighbors for target users. A collaborative filtering recommendation algorithm MCQ-CF based on user model is present. The experiment results show that the algorithm MCQ-CF has good performance for accuracy and stability.