{"title":"基于mapreduce的并行推荐算法的研究与设计","authors":"Juan Yang, Han Du, Bin Wu, Xinxin Ge","doi":"10.1109/CCIS.2012.6664417","DOIUrl":null,"url":null,"abstract":"The rapid development of Internet technology has brought the problem of information overload, and recommendation algorithm is put forward and considered to be the most effective way to solve the problem. Most of the traditional research about recommendation algorithm is focused on accuracy and diversity. However, in the practical engineering application, massive data process will be the most serious problem. In this paper, we propose a parallel recommendation algorithm based on mapreduce programming model, which runs on Hadoop platform, and in our system, we use mongodb as our auxiliary storage data. Finally, we give some experiments to prove our algorithm is suitable for processing massive data.","PeriodicalId":392558,"journal":{"name":"2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems","volume":"51 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"The research and design of parallel recommendation algorithm based on mapreduce\",\"authors\":\"Juan Yang, Han Du, Bin Wu, Xinxin Ge\",\"doi\":\"10.1109/CCIS.2012.6664417\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The rapid development of Internet technology has brought the problem of information overload, and recommendation algorithm is put forward and considered to be the most effective way to solve the problem. Most of the traditional research about recommendation algorithm is focused on accuracy and diversity. However, in the practical engineering application, massive data process will be the most serious problem. In this paper, we propose a parallel recommendation algorithm based on mapreduce programming model, which runs on Hadoop platform, and in our system, we use mongodb as our auxiliary storage data. Finally, we give some experiments to prove our algorithm is suitable for processing massive data.\",\"PeriodicalId\":392558,\"journal\":{\"name\":\"2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems\",\"volume\":\"51 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CCIS.2012.6664417\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCIS.2012.6664417","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The research and design of parallel recommendation algorithm based on mapreduce
The rapid development of Internet technology has brought the problem of information overload, and recommendation algorithm is put forward and considered to be the most effective way to solve the problem. Most of the traditional research about recommendation algorithm is focused on accuracy and diversity. However, in the practical engineering application, massive data process will be the most serious problem. In this paper, we propose a parallel recommendation algorithm based on mapreduce programming model, which runs on Hadoop platform, and in our system, we use mongodb as our auxiliary storage data. Finally, we give some experiments to prove our algorithm is suitable for processing massive data.